BibSLEIGH — vector stem

Used together with:

support (245)
machin (221)
use (120)
base (112)
model (54)

Stem vector$ (all stems)

656 papers:

DAC-2015-Pomeranz #generative #testing: Generation of close-to-functional broadside tests with equal primary input vectors (IP), p. 6.
SIGMOD-2015-PolychroniouRR #database #in memory: Rethinking SIMD Vectorization for In-Memory Databases (OP, AR, KAR), pp. 1493–1508.
ESOP-2015-DavidKL #source code #strict #termination: Unrestricted Termination and Non-termination Arguments for Bit-Vector Programs (CD, DK, ML), pp. 183–204.
PLDI-2015-RenJKAK #execution #hardware #performance #recursion #source code: Efficient execution of recursive programs on commodity vector hardware (BR, YJ, SK, KA, MK), pp. 509–520.
STOC-2015-AggarwalDRS #problem #using: Solving the Shortest Vector Problem in 2n Time Using Discrete Gaussian Sampling: Extended Abstract (DA, DD, OR, NSD), pp. 733–742.
STOC-2015-HenzingerKNS #multi #online #problem: Unifying and Strengthening Hardness for Dynamic Problems via the Online Matrix-Vector Multiplication Conjecture (MH, SK, DN, TS), pp. 21–30.
ICALP-v2-2015-LerouxST #automaton #on the #problem: On the Coverability Problem for Pushdown Vector Addition Systems in One Dimension (JL, GS, PT), pp. 324–336.
ICFP-2015-StuckiRUB #sequence: RRB vector: a practical general purpose immutable sequence (NS, TR, VU, PB), pp. 342–354.
CAiSE-2015-AbubahiaC #approach #clustering: A Clustering Approach for Protecting GIS Vector Data (AA, MC), pp. 133–147.
ICML-2015-GeigerZSGJ #component #identification #process: Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components (PG, KZ, BS, MG, DJ), pp. 1917–1925.
ICML-2015-QiuXHLC #estimation #matrix #process #robust: Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes (HQ, SX, FH, HL, BC), pp. 1843–1851.
ICML-2015-TanseyPSR #exponential #markov #product line #random: Vector-Space Markov Random Fields via Exponential Families (WT, OHMP, ASS, PR), pp. 684–692.
MLDM-2015-CostaFK #multi #using: Classifying Grasslands and Cultivated Pastures in the Brazilian Cerrado Using Support Vector Machines, Multilayer Perceptrons and Autoencoders (WC, LMGF, TSK), pp. 187–198.
MLDM-2015-GovadaJMS #approach #hybrid #induction #learning #using: Hybrid Approach for Inductive Semi Supervised Learning Using Label Propagation and Support Vector Machine (AG, PJ, SM, SKS), pp. 199–213.
OOPSLA-2015-LeeBZZVG #assessment #float #named #runtime: RAIVE: runtime assessment of floating-point instability by vectorization (WCL, TB, YZ, XZ, KV, RG), pp. 623–638.
OOPSLA-2015-WangPW: Vectorization of apply to reduce interpretation overhead of R (HW, DAP, PW), pp. 400–415.
SAC-2015-ChaudhuriMG #network #predict #using: QoS prediction for network data traffic using hierarchical modified regularized least squares rough support vector regression (AC, SM, SKG), pp. 659–661.
SAC-2015-OmatuYI #classification #learning #smell: Smell classification of wines by the learning vector quantization method (SO, MY, YI), pp. 195–200.
SAC-2015-RafailidisM #image #parallel #similarity: Parallel similarity search based on the dimensions value cardinalities of image descriptor vectors (DR, YM), pp. 1023–1030.
SAC-2015-Rekha #big data #detection #performance #using: A fast support vector data description system for anomaly detection using big data (AGR), pp. 931–932.
CGO-2015-PorpodasMJ #automation #named: PSLP: padded SLP automatic vectorization (VP, AM, TMJ), pp. 190–201.
CGO-2015-TangZLLHLG #multi #optimisation: Optimizing and auto-tuning scale-free sparse matrix-vector multiplication on Intel Xeon Phi (WTT, RZ, ML, YL, HPH, XL, RSMG), pp. 136–145.
LICS-2015-BlondinFGHM #2d #reachability: Reachability in Two-Dimensional Vector Addition Systems with States Is PSPACE-Complete (MB, AF, SG, CH, PM), pp. 32–43.
LICS-2015-BrazdilKKN #behaviour #probability: Long-Run Average Behaviour of Probabilistic Vector Addition Systems (TB, SK, AK, PN), pp. 44–55.
LICS-2015-LerouxS #reachability: Demystifying Reachability in Vector Addition Systems (JL, SS), pp. 56–67.
CASE-2014-LiuLS #monitoring: A wavelet-based characteristic vector construction method for machining condition monitoring (CL, YL, WS), pp. 304–308.
DATE-2014-DuricPSUCVB #execution #named #power management: EVX: Vector execution on low power EDGE cores (MD, OP, AS, OSÜ, AC, MV, DB), pp. 1–4.
ICSME-2014-WangLL #composition #debugging #locality #modelling: Compositional Vector Space Models for Improved Bug Localization (SW, DL, JL), pp. 171–180.
PEPM-J-2013-Simon14 #type inference #using: Deriving a complete type inference for Hindley-Milner and vector sizes using expansion (AS), pp. 254–271.
FLOPS-2014-BarkatiWJ #interpreter #multi #named: Faustine: A Vector Faust Interpreter Test Bed for Multimedia Signal Processing — System Description (KB, HW, PJ), pp. 69–85.
IFL-2014-AchtenSDP #composition #interactive #programming #scalability: Task Oriented Programming with Purely Compositional Interactive Scalable Vector Graphics (PA, JS, LD, RP), p. 7.
GRAPHITE-2014-DelzannoRT #analysis #protocol: Graph- versus Vector-Based Analysis of a Consensus Protocol (GD, AR, RT), pp. 44–57.
ICEIS-v2-2014-KaramtiTG #image #network #process #retrieval #using: Vectorization of Content-based Image Retrieval Process Using Neural Network (HK, MT, FG), pp. 435–439.
CIKM-2014-AlabdulmohsinGZ #reverse engineering #robust: Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering (IMA, XG, XZ), pp. 231–240.
ICML-c1-2014-HsiehSD #divide and conquer #kernel: A Divide-and-Conquer Solver for Kernel Support Vector Machines (CJH, SS, ISD), pp. 566–574.
ICML-c2-2014-LinYHY #distance #learning: Geodesic Distance Function Learning via Heat Flow on Vector Fields (BL, JY, XH, JY), pp. 145–153.
ICML-c2-2014-TorkamaniL #on the #robust: On Robustness and Regularization of Structural Support Vector Machines (MT, DL), pp. 577–585.
ICPR-2014-AggarwalM #algorithm #metric #multi #random: Extension of Sparse Randomized Kaczmarz Algorithm for Multiple Measurement Vectors (HKA, AM), pp. 1014–1019.
ICPR-2014-ArvanitopoulosBT #analysis #learning: Laplacian Support Vector Analysis for Subspace Discriminative Learning (NA, DB, AT), pp. 1609–1614.
ICPR-2014-BaecchiTSBB #random #recognition: Fisher Vectors over Random Density Forests for Object Recognition (CB, FT, LS, ADB, ADB), pp. 4328–4333.
ICPR-2014-BlondelFU #multi #scalability: Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex (MB, AF, NU), pp. 1289–1294.
ICPR-2014-GavriilidisT #classification #kernel #random #using: Random Walk Kernel Applications to Classification Using Support Vector Machines (VG, AT), pp. 3898–3903.
ICPR-2014-LiuGZL #novel #robust: A Novel Robust Modified Support Vector Machines (SL, JG, SZ, YL), pp. 3834–3838.
ICPR-2014-SariE #analysis #detection #fault #independence #using: Texture Defect Detection Using Independent Vector Analysis in Wavelet Domain (LS, AE), pp. 1639–1644.
ICPR-2014-SjolundJAKN #segmentation: Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features (JS, AEJ, MTA, HK, HN), pp. 3274–3279.
ICPR-2014-WangSWB #consistency: Label Consistent Fisher Vectors for Supervised Feature Aggregation (QW, XS, MW, KLB), pp. 3588–3593.
ICPR-2014-XieUKG #incremental #learning: Incremental Learning with Support Vector Data Description (WX, SU, SK, MG), pp. 3904–3909.
KDD-2014-ZhaoLC #performance: Safe and efficient screening for sparse support vector machine (ZZ, JL, JC), pp. 542–551.
KMIS-2014-HisakaneOSK #multi: A Tutoring Rule Selection Method for Case-based e-Learning by Multi-class Support Vector Machine (DH, MO, MS, NK), pp. 119–125.
MLDM-2014-MountassirBB #algorithm #classification #documentation #representation: The Nearest Centroid Based on Vector Norms: A New Classification Algorithm for a New Document Representation Model (AM, HB, IB), pp. 442–456.
GPCE-2014-KaminGAXYC #multi #optimisation #runtime: Optimization by runtime specialization for sparse matrix-vector multiplication (SK, MJG, BA, DX, BY, ZC), pp. 93–102.
SAC-2014-SeelandMKK #classification: Extracting information from support vector machines for pattern-based classification (MS, AM, AK, SK), pp. 129–136.
SAC-2014-YoonKHKRC #metric #reachability #similarity: Reachability vectors: features for link-based similarity measures (SHY, JSK, JH, SWK, MR, HJC), pp. 594–597.
FSE-2014-MileaJK #abstraction #detection #refactoring #scalability: Vector abstraction and concretization for scalable detection of refactorings (NAM, LJ, SCK), pp. 86–97.
CAV-2014-HadareanBJBT #lazy evaluation: A Tale of Two Solvers: Eager and Lazy Approaches to Bit-Vectors (LH, KB, DJ, CB, CT), pp. 680–695.
CAV-2014-Nadel #automation #generative: Bit-Vector Rewriting with Automatic Rule Generation (AN), pp. 663–679.
LICS-CSL-2014-LerouxPS #automaton #bound: Hyper-Ackermannian bounds for pushdown vector addition systems (JL, MP, GS), p. 10.
CASE-2013-ShenWWW #modelling #optimisation: Application of vector ordinal optimization to the transportation systems with agent based modelling (ZS, KW, FYW, KW), pp. 898–903.
CASE-2013-SustoSPPMB #fault #maintenance #predict: A predictive maintenance system for integral type faults based on support vector machines: An application to ion implantation (GAS, AS, SP, DP, SFM, AB), pp. 195–200.
DATE-2013-ChaG #approach #detection #effectiveness #metric: Trojan detection via delay measurements: a new approach to select paths and vectors to maximize effectiveness and minimize cost (BC, SKG), pp. 1265–1270.
DATE-2013-JongheDDG #modelling #recursion: Extracting analytical nonlinear models from analog circuits by recursive vector fitting of transfer function trajectories (DdJ, DD, TD, GGEG), pp. 1448–1453.
DATE-2013-QianJBTMM #analysis #named #performance #using: SVR-NoC: a performance analysis tool for network-on-chips using learning-based support vector regression model (ZQ, DCJ, PB, CYT, DM, RM), pp. 354–357.
DRR-2013-SimistiraPKC #analysis #online: Structural analysis of online handwritten mathematical symbols based on support vector machines (FS, VP, VK, GC).
ICDAR-2013-FielS #identification #retrieval #using #visual notation: Writer Identification and Writer Retrieval Using the Fisher Vector on Visual Vocabularies (SF, RS), pp. 545–549.
ICDAR-2013-MaraK #3d #invariant: Vectorization of 3D-Characters by Integral Invariant Filtering of High-Resolution Triangular Meshes (HM, SK), pp. 62–66.
TACAS-2013-JohnC #linear #quantifier: Extending Quantifier Elimination to Linear Inequalities on Bit-Vectors (AKJ, SC), pp. 78–92.
PEPM-2013-Simon #type inference #using: Deriving a complete type inference for hindley-milner and vector sizes using expansion (AS), pp. 13–22.
PLDI-2013-LiTCS #adaptation #multi #named: SMAT: an input adaptive auto-tuner for sparse matrix-vector multiplication (JL, GT, MC, NS), pp. 117–126.
STOC-2013-AzarCKS #bound #online: Tight bounds for online vector bin packing (YA, IRC, SK, FBS), pp. 961–970.
STOC-2013-BhowmickDL #bound #product line: New bounds for matching vector families (AB, ZD, SL), pp. 823–832.
STOC-2013-HardtR #analysis #worst-case: Beyond worst-case analysis in private singular vector computation (MH, AR), pp. 331–340.
ICFP-2013-MainlandLJ: Exploiting vector instructions with generalized stream fusio (GM, RL, SLPJ), pp. 37–48.
ICFP-2013-PetersenOG #automation #haskell: Automatic SIMD vectorization for Haskell (LP, DAO, NG), pp. 25–36.
HCI-III-2013-ZhanZSY #image #multi #re-engineering #using #visual notation: Visual Image Reconstruction from fMRI Activation Using Multi-scale Support Vector Machine Decoders (YZ, JZ, SS, LY), pp. 491–497.
HIMI-HSM-2013-SaitohI #detection #learning #using #visualisation: Visualization of Anomaly Data Using Peculiarity Detection on Learning Vector Quantization (FS, SI), pp. 181–188.
ICML-c1-2013-CotterSS #learning: Learning Optimally Sparse Support Vector Machines (AC, SSS, NS), pp. 266–274.
ICML-c1-2013-DoK: Convex formulations of radius-margin based Support Vector Machines (HD, AK), pp. 169–177.
ICML-c2-2013-MinhBM #framework #learning #multi: A unifying framework for vector-valued manifold regularization and multi-view learning (HQM, LB, VM), pp. 100–108.
ICML-c3-2013-GopiN0N: One-Bit Compressed Sensing: Provable Support and Vector Recovery (SG, PN, PJ, AVN), pp. 154–162.
ICML-c3-2013-OgawaITS: Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines (KO, MI, IT, MS), pp. 897–905.
ICML-c3-2013-OgawaST: Safe Screening of Non-Support Vectors in Pathwise SVM Computation (KO, YS, IT), pp. 1382–1390.
KDD-2013-LeeNBC #predict #social: Link prediction with social vector clocks (CL, BN, UB, PC), pp. 784–792.
KDD-2013-NarasimhanA #bound #named #optimisation: SVMpAUCtight: a new support vector method for optimizing partial AUC based on a tight convex upper bound (HN, SA), pp. 167–175.
KDD-2013-RistanoskiLB: A time-dependent enhanced support vector machine for time series regression (GR, WL, JB), pp. 946–954.
KDIR-KMIS-2013-ChenYTH #behaviour #predict: The Disulfide Connectivity Prediction with Support Vector Machine and Behavior Knowledge Space (HYC, CBY, KTT, CYH), pp. 112–118.
KDIR-KMIS-2013-Lindner #constraints #parsing #random #using: Using Conditional Random Fields with Constraints to Train Support Vector Machines — Locating and Parsing Bibliographic References (SL), pp. 28–36.
MLDM-2013-MinhAN #algorithm #feature model: DCA Based Algorithms for Feature Selection in Semi-supervised Support Vector Machines (LHM, LTHA, MCN), pp. 528–542.
MLDM-2013-OnoderaS #kernel: The Gapped Spectrum Kernel for Support Vector Machines (TO, TS), pp. 1–15.
MLDM-2013-PoziMD #estimation #predict: Density Ratio Estimation in Support Vector Machine for Better Generalization: Study on Direct Marketing Prediction (MSMP, AM, AD), pp. 275–280.
MLDM-2013-SouzaP #random #recognition #word: Sign Language Recognition with Support Vector Machines and Hidden Conditional Random Fields: Going from Fingerspelling to Natural Articulated Words (CRdS, EBP), pp. 84–98.
MLDM-2013-StambaughYB #feature model: Analytic Feature Selection for Support Vector Machines (CS, HY, FB), pp. 219–233.
SAC-2013-AbrilNT #documentation #towards: Towards a private vector space model for confidential documents (DA, GNA, VT), pp. 944–945.
SAC-2013-CavalinKMO #framework #multi #recognition: A multiple feature vector framework for forest species recognition (PRC, MNK, JM, LESdO), pp. 16–20.
SAC-2013-ChenLWW #abstraction #source code #static analysis: Static analysis of list-manipulating programs via bit-vectors and numerical abstractions (LC, RL, XW, JW), pp. 1204–1210.
SAC-2013-MadeoLP #gesture #rest #segmentation #using: Gesture unit segmentation using support vector machines: segmenting gestures from rest positions (RCBM, CAML, SMP), pp. 46–52.
CC-2013-CammarotaNVKDM #on the #optimisation: On the Determination of Inlining Vectors for Program Optimization (RC, AN, AVV, AK, DD, MM), pp. 164–183.
CGO-2013-JeonGT #named #performance #program analysis: Skadu: Efficient vector shadow memories for poly-scopic program analysis (DJ, SG, MBT), p. 12.
CADE-2013-KovasznaiFB #quantifier: : A Tool for Polynomially Translating Quantifier-Free Bit-Vector Formulas into (GK, AF, AB), pp. 443–449.
LICS-2013-Leroux: Presburger Vector Addition Systems (JL), pp. 23–32.
LICS-2013-LerouxPS #on the #problem: On the Context-Freeness Problem for Vector Addition Systems (JL, VP, GS), pp. 43–52.
ASE-2012-MaigaABSGAA #anti #detection: Support vector machines for anti-pattern detection (AM, NA, NB, AS, YGG, GA, EA), pp. 278–281.
CASE-2012-ZhangWZX #fault #memory management #using: A dynamic memory model for mechanical fault diagnosis using one-class support vector machine (QZ, JW, JZ, GX), pp. 497–501.
DATE-2012-Voyiatzis #concurrent #logic #monitoring #multi: Input vector monitoring on line concurrent BIST based on multilevel decoding logic (IV), pp. 1251–1256.
VLDB-2012-MetwallyF #framework #multi #named #pipes and filters #scalability #similarity: V-SMART-Join: A Scalable MapReduce Framework for All-Pair Similarity Joins of Multisets and Vectors (AM, CF), pp. 704–715.
WCRE-2012-JanaN #detection #dynamic analysis #precise #using: Precise Detection of Uninitialized Variables Using Dynamic Analysis — Extending to Aggregate and Vector Types (AJ, RN), pp. 197–201.
PLDI-2012-HolewinskiRRFPRS #analysis: Dynamic trace-based analysis of vectorization potential of applications (JH, RR, MR, NF, LNP, AR, PS), pp. 371–382.
STOC-2012-KhotPV #preprocessor #problem: 2log1-ε n hardness for the closest vector problem with preprocessing (SK, PP, NKV), pp. 277–288.
ICALP-v1-2012-DeshpandeKS: Zero-One Rounding of Singular Vectors (AD, RK, NS), pp. 278–289.
ICEIS-v1-2012-Wichert #fault #retrieval: Product Quantization for Vector Retrieval with No Error (AW), pp. 87–92.
CIKM-2012-WangSGM #adaptation #collaboration: Adapting vector space model to ranking-based collaborative filtering (SW, JS, BJG, JM), pp. 1487–1491.
CIKM-2012-ZhuZYGX #classification #parallel: Parallel proximal support vector machine for high-dimensional pattern classification (ZZ, XZ, YY, YFG, XX), pp. 2351–2354.
ICML-2012-BiggioNL: Poisoning Attacks against Support Vector Machines (BB, BN, PL), p. 190.
ICML-2012-DhillonRFU #modelling #using #word: Using CCA to improve CCA: A new spectral method for estimating vector models of words (PSD, JR, DPF, LHU), p. 13.
ICML-2012-Rakotomamonjy #infinity: Sparse Support Vector Infinite Push (AR), p. 48.
ICPR-2012-AryafarJS #automation #classification #using: Automatic musical genre classification using sparsity-eager support vector machines (KA, SJ, AS), pp. 1526–1529.
ICPR-2012-DriraADSB #3d #novel #random #recognition: 3D dynamic expression recognition based on a novel Deformation Vector Field and Random Forest (HD, BBA, MD, AS, SB), pp. 1104–1107.
ICPR-2012-FefilatyevSKHGKDRB #reduction: Label-noise reduction with support vector machines (SF, MS, KK, LOH, DBG, RK, KD, AR, HB), pp. 3504–3508.
ICPR-2012-Gao12a #clustering #estimation #multi #using: Facial age estimation using Clustered Multi-task Support Vector Regression Machine (PXG), pp. 541–544.
ICPR-2012-GuSLF #metric: Discriminative metric: Schatten norm vs. vector norm (ZG, MS, LL, YF), pp. 1213–1216.
ICPR-2012-Ho #approach #detection #effectiveness: An effective vortex detection approach for velocity vector field (SSH), pp. 2643–2646.
ICPR-2012-HuangLW #clustering #detection #incremental: Incremental support vector clustering with outlier detection (DH, JHL, CDW), pp. 2339–2342.
ICPR-2012-JinGYZ #algorithm #learning #multi: Multi-label learning vector quantization algorithm (XBJ, GG, JY, DZ), pp. 2140–2143.
ICPR-2012-JiSL #multi: Multitask multiclass privileged information support vector machines (YJ, SS, YL), pp. 2323–2326.
ICPR-2012-KiwanukaW #clustering: Cluster-based vector-attribute filtering for CT and MRI enhancement (FNK, MHFW), pp. 3112–3115.
ICPR-2012-OrfanidisT #subclass: Exploiting subclass information in Support Vector Machines (GO, AT), pp. 1076–1079.
ICPR-2012-PhanST #detection #symmetry #using: Text detection in natural scenes using Gradient Vector Flow-Guided symmetry (TQP, PS, CLT), pp. 3296–3299.
ICPR-2012-SeredinMTRW #multimodal #pattern matching #pattern recognition #recognition: Convex support and Relevance Vector Machines for selective multimodal pattern recognition (OS, VM, AT, NR, DW), pp. 1647–1650.
ICPR-2012-ShiraiOI #component #composition #image: Color-line vector field and local color component decomposition for smoothing and denoising of color images (KS, MO, MI), pp. 3050–3053.
ICPR-2012-YangGAZW #classification #clustering #query: Iterative clustering and support vectors-based high-confidence query selection for motor imagery EEG signals classification (HY, CG, KKA, HZ, CW), pp. 2169–2172.
KDD-2012-MatsushimaVS #linear: Linear support vector machines via dual cached loops (SM, SVNV, AJS), pp. 177–185.
KDD-2012-ZhouKTX #machine learning: Adversarial support vector machine learning (YZ, MK, BMT, BX), pp. 1059–1067.
KEOD-2012-FigueirasCPLJ #approach #collaboration #information retrieval: Information Retrieval in Collaborative Engineering Projects — A Vector Space Model Approach (PF, RC, LP, CL, RJG), pp. 233–238.
MLDM-2012-NguyenF #programming: A General Lp-norm Support Vector Machine via Mixed 0-1 Programming (HTN, KF), pp. 40–49.
MLDM-2012-PitelisT #learning: Discriminant Subspace Learning Based on Support Vectors Machines (NP, AT), pp. 198–212.
MLDM-2012-SinghCS #approach #hybrid #performance #recognition #using: A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines (LS, GC, DS), pp. 660–668.
MLDM-2012-ToussaintB #comparison #empirical #learning: Proximity-Graph Instance-Based Learning, Support Vector Machines, and High Dimensionality: An Empirical Comparison (GTT, CB), pp. 222–236.
SEKE-2012-LiuCL #concurrent #in the cloud: Singular Formulas for Compound Siphons, Complementary Siphons and Characteristic Vectors for Deadlock Prevention in Cloud Computing (GL, DYC, YNL), pp. 359–362.
SEKE-2012-Lozano-FuentesGBSEWHGT #using: Using Cell Phones for Mosquito Vector Surveillance and Control (SLF, SG, JMB, DS, LE, FW, EHG, JGR, DTC), pp. 763–767.
SAC-2012-MirizziNSR #recommendation #semantics #web: Web 3.0 in action: Vector Space Model for semantic (movie) Recommendations (RM, TDN, EDS, AR), pp. 403–405.
SAC-2012-SarroMFG #algorithm #analysis #fault #predict #search-based: A further analysis on the use of Genetic Algorithm to configure Support Vector Machines for inter-release fault prediction (FS, SDM, FF, CG), pp. 1215–1220.
CGO-2012-KerrDY #compilation #kernel: Dynamic compilation of data-parallel kernels for vector processors (AK, GFD, SY), pp. 23–32.
IJCAR-2012-SpielmannK #bound #synthesis: Synthesis for Unbounded Bit-Vector Arithmetic (AS, VK), pp. 499–513.
SAT-2012-AbalCHP #problem #term rewriting #using: Using Term Rewriting to Solve Bit-Vector Arithmetic Problems — (Poster Presentation) (IA, AC, JH, JSP), pp. 493–495.
SMT-2012-KovasznaiFB #complexity #logic #on the: On the Complexity of Fixed-Size Bit-Vector Logics with Binary Encoded Bit-Width (GK, AF, AB), pp. 44–56.
DATE-2011-BarceloGBS #estimation #performance #scalability: An efficient and scalable STA tool with direct path estimation and exhaustive sensitization vector exploration for optimal delay computation (SB, XG, SAB, JS), pp. 1602–1607.
DATE-2011-HuangWSLXL #embedded #low cost: A specialized low-cost vectorized loop buffer for embedded processors (LH, ZW, LS, HL, NX, CL), pp. 1200–1203.
DRR-2011-KimLT #automation #identification #using: Automated identification of biomedical article type using support Vector machines (ICK, DXL, GRT), pp. 1–10.
ICDAR-2011-KumarD #image #performance #using: Fast Rule-Line Removal Using Integral Images and Support Vector Machines (JK, DSD), pp. 584–588.
ICDAR-2011-LinGC #component #documentation: Classifying Textual Components of Bilingual Documents with Decision-Tree Support Vector Machines (XRL, CYG, FC), pp. 498–502.
ICDAR-2011-PhanSST #segmentation #video: A Gradient Vector Flow-Based Method for Video Character Segmentation (TQP, PS, BS, CLT), pp. 1024–1028.
ICDAR-2011-WakaharaK #clustering #image #string #using: Binarization of Color Character Strings in Scene Images Using K-Means Clustering and Support Vector Machines (TW, KK), pp. 274–278.
SIGMOD-2011-SchaikM #data type #memory management #performance #reachability: A memory efficient reachability data structure through bit vector compression (SJvS, OdM), pp. 913–924.
SIGMOD-2011-YuKKHH: Exact indexing for support vector machines (HY, IK, YK, SwH, WSH), pp. 709–720.
VLDB-2011-YangPS #graph #mining #multi #performance: Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining (XY, SP, PS), pp. 231–242.
LATA-2011-Leroux #problem #proving #reachability #self: Vector Addition System Reachability Problem: A Short Self-contained Proof (JL), pp. 41–64.
HIMI-v1-2011-KlimaS #android: Vector Keyboard for Android Platform-Based Devices (MK, PS), pp. 435–442.
ICEIS-v2-2011-LiangZL: Stock Market Forecasting based on Wavelet and Least Squares Support Vector Machine (XL, HZ, XL), pp. 46–53.
ICEIS-v2-2011-ZhuL: Mutual Interdependence of Stock Markets based on Support Vector Machine (MZ, JL), pp. 218–221.
CIKM-2011-ZhouH #framework #markov #novel: A novel framework of training hidden markov support vector machines from lightly-annotated data (DZ, YH), pp. 2025–2028.
ICML-2011-CoatesN #encoding: The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization (AC, AYN), pp. 921–928.
ICML-2011-FrancZS #modelling #probability: Support Vector Machines as Probabilistic Models (VF, AZ, BS), pp. 665–672.
ICML-2011-KarasuyamaT #algorithm: Suboptimal Solution Path Algorithm for Support Vector Machine (MK, IT), pp. 473–480.
ICML-2011-LadickyT #linear: Locally Linear Support Vector Machines (LL, PHST), pp. 985–992.
ICML-2011-MinhS: Vector-valued Manifold Regularization (HQM, VS), pp. 57–64.
KDIR-2011-ReuterC #identification #learning #similarity #using: Learning Similarity Functions for Event Identification using Support Vector Machines (TR, PC), pp. 208–215.
MLDM-2011-Benbrahim #fuzzy: Fuzzy Semi-supervised Support Vector Machines (HB), pp. 127–139.
RecSys-2011-KimE #personalisation #rank #recommendation: Personalized PageRank vectors for tag recommendations: inside FolkRank (HNK, AES), pp. 45–52.
SEKE-2011-WangKN #empirical #metric #using: An Empirical Study of Software Metrics Selection Using Support Vector Machine (HW, TMK, AN), pp. 83–88.
SEKE-2011-ZhaoZWY #fault #locality #named #reduction: PAFL: Fault Localization via Noise Reduction on Coverage Vector (LZ, ZZ, LW, XY), pp. 203–206.
POPL-2011-Leroux #problem #proving #reachability #self: Vector addition system reachability problem: a short self-contained proof (JL), pp. 307–316.
CC-2011-HenrettySPFRS #architecture #layout: Data Layout Transformation for Stencil Computations on Short-Vector SIMD Architectures (TH, KS, LNP, FF, JR, PS), pp. 225–245.
CGO-2011-KarrenbergH: Whole-function vectorization (RK, SH), pp. 141–150.
CGO-2011-NuzmanDRRWYCZ: Vapor SIMD: Auto-vectorize once, run everywhere (DN, SD, ER, IR, KW, DY, AC, AZ), pp. 151–160.
DAC-2010-El-MoselhyD #probability: Stochastic dominant singular vectors method for variation-aware extraction (TAEM, LD), pp. 667–672.
DAC-2010-HsuPH #approach #data flow #modelling #physics #simulation: A mixed-mode vector-based dataflow approach for modeling and simulating LTE physical layer (CJH, JLP, FJH), pp. 18–23.
DAC-2010-LimKH #functional #generative #modelling #performance: An efficient test vector generation for checking analog/mixed-signal functional models (BL, JK, MAH), pp. 767–772.
DATE-2010-Jerger #named: SigNet: Network-on-chip filtering for coarse vector directories (NDEJ), pp. 1378–1383.
DATE-2010-PomeranzR #requirements #sequence #testing #using: Reducing the storage requirements of a test sequence by using a background vector (IP, SMR), pp. 1237–1242.
TACAS-2010-BardinHP #satisfiability: An Alternative to SAT-Based Approaches for Bit-Vectors (SB, PH, FP), pp. 84–98.
TACAS-2010-CookKRW #ranking #synthesis: Ranking Function Synthesis for Bit-Vector Relations (BC, DK, PR, CMW), pp. 236–250.
ICPC-2010-ScannielloDDD #algorithm #clustering #using: Using the Kleinberg Algorithm and Vector Space Model for Software System Clustering (GS, AD, CD, TD), pp. 180–189.
ICALP-v2-2010-BrazdilJK #game studies #reachability: Reachability Games on Extended Vector Addition Systems with States (TB, PJ, AK), pp. 478–489.
CIKM-2010-XiaoLC #bound #concept: K-farthest-neighbors-based concept boundary determination for support vector data description (YX, BL, LC), pp. 1701–1704.
ICML-2010-TuL #classification #multi: One-sided Support Vector Regression for Multiclass Cost-sensitive Classification (HHT, HTL), pp. 1095–1102.
ICPR-2010-BrewC #verification: Vector Quantization Mappings for Speaker Verification (AB, PC), pp. 560–564.
ICPR-2010-CadavidA #automation #detection #modelling #using #visual notation: Exploiting Visual Quasi-periodicity for Automated Chewing Event Detection Using Active Appearance Models and Support Vector Machines (SC, MAM), pp. 1714–1717.
ICPR-2010-ChenXF: Optimized Entropy-constrained Vector Quantization of lossy Vector Map Compression (MC, MX, PF), pp. 722–725.
ICPR-2010-Fehr #3d #invariant: Local Rotation Invariant Patch Descriptors for 3D Vector Fields (JF), pp. 1381–1384.
ICPR-2010-GuoBC #approach #learning #using: Support Vectors Selection for Supervised Learning Using an Ensemble Approach (LG, SB, NC), pp. 37–40.
ICPR-2010-HeG #classification: Rare Class Classification by Support Vector Machine (HH, AG), pp. 548–551.
ICPR-2010-KappSM #adaptation #incremental #learning: Adaptive Incremental Learning with an Ensemble of Support Vector Machines (MNK, RS, PM), pp. 4048–4051.
ICPR-2010-KeuperBRPH #3d #robust: Mean Shift Gradient Vector Flow: A Robust External Force Field for 3D Active Surfaces (MK, HB, OR, JP, PH), pp. 2784–2787.
ICPR-2010-KitaW #clustering #image #using: Binarization of Color Characters in Scene Images Using k-means Clustering and Support Vector Machines (KK, TW), pp. 3183–3186.
ICPR-2010-LiPBT #2d #recognition: Improved Facial Expression Recognition with Trainable 2-D Filters and Support Vector Machines (PL, SLP, AB, FHCT), pp. 3732–3735.
ICPR-2010-LiS #kernel #multi: Nonlinear Combination of Multiple Kernels for Support Vector Machines (JL, SS), pp. 2889–2892.
ICPR-2010-MoonQ #effectiveness #reduction: Effective Dimensionality Reduction Based on Support Vector Machine (SM, HQ), pp. 173–176.
ICPR-2010-NgPS #automation #clustering #fuzzy: Automated Feature Weighting in Fuzzy Declustering-based Vector Quantization (TFN, TDP, CS), pp. 686–689.
ICPR-2010-NikitidisNP #incremental #multi: Incremental Training of Multiclass Support Vector Machines (SN, NN, IP), pp. 4267–4270.
ICPR-2010-OzcanG #detection #fuzzy: Fuzzy Support Vector Machines for ECG Arrhythmia Detection (NOO, FSG), pp. 2973–2976.
ICPR-2010-PlazaP #image #order: Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images (AP, JP), pp. 4412–4415.
ICPR-2010-RahtuSH #random #using: Compressing Sparse Feature Vectors Using Random Ortho-Projections (ER, MS, JH), pp. 1397–1400.
ICPR-2010-RichiardiVRB #classification #graph #sequence: Vector Space Embedding of Undirected Graphs with Fixed-cardinality Vertex Sequences for Classification (JR, DVDV, KR, HB), pp. 902–905.
ICPR-2010-SagheerHNS #recognition #using #word: Holistic Urdu Handwritten Word Recognition Using Support Vector Machine (MWS, CLH, NN, CYS), pp. 1900–1903.
ICPR-2010-TasdemirC #detection #video: Motion Vector Based Features for Content Based Video Copy Detection (KT, AEÇ), pp. 3134–3137.
ICPR-2010-WakaharaU #composition #recognition: Hierarchical Decomposition of Handwriting Deformation Vector Field for Improving Recognition Accuracy (TW, SU), pp. 1860–1863.
ICPR-2010-XuCM #segmentation: An Improved Fluid Vector Flow for Cavity Segmentation in Chest Radiographs (TX, IC, MKM), pp. 3376–3379.
ICPR-2010-ZhangZZZ #classification #kernel #metric #using: Time Series Classification Using Support Vector Machine with Gaussian Elastic Metric Kernel (DZ, WZ, DZ, HZ), pp. 29–32.
KDD-2010-LinC #outsourcing #privacy #random: Privacy-preserving outsourcing support vector machines with random transformation (KPL, MSC), pp. 363–372.
KDD-2010-YangO #feature model #predict #probability #using: Feature selection for support vector regression using probabilistic prediction (JBY, CJO), pp. 343–352.
KDIR-2010-Schoneberg #classification #evaluation: Context Vector Classification — Term Classification with Context Evaluation (HS), pp. 387–391.
RecSys-2010-Musto #modelling #recommendation: Enhanced vector space models for content-based recommender systems (CM), pp. 361–364.
SEKE-2010-FagundesS #estimation #fault #using: Software Defect Estimation using Support Vector Regression (RAdAF, RMCRdS), pp. 265–268.
SIGIR-2010-RadovanovicNI #modelling #on the: On the existence of obstinate results in vector space models (MR, AN, MI), pp. 186–193.
SAC-2010-FautschS #adaptation #information retrieval: Adapting the tf idf vector-space model to domain specific information retrieval (CF, JS), pp. 1708–1712.
SAC-2010-SillaKK #automation #classification #hybrid #music: Improving automatic music genre classification with hybrid content-based feature vectors (CNSJ, ALK, CAAK), pp. 1702–1707.
PPoPP-2010-ChoiSV #modelling #multi: Model-driven autotuning of sparse matrix-vector multiply on GPUs (JC, AS, RWV), pp. 115–126.
PPoPP-2010-JangMSDK #architecture #data transformation #parallel #thread: Data transformations enabling loop vectorization on multithreaded data parallel architectures (BJ, PM, DS, RD, DRK), pp. 353–354.
IJCAR-2010-MaricJ #named #reduction: URBiVA: Uniform Reduction to Bit-Vector Arithmetic (FM, PJ), pp. 346–352.
DATE-2009-ChenKLA: Accelerating FPGA-based emulation of quasi-cyclic LDPC codes with vector processing (XC, JK, SL, VA), pp. 1530–1535.
DRR-2009-LiD #difference #independence #using #verification: Improving semi-text-independent method of writer verification using difference vector (XL, XD), pp. 1–10.
HT-2009-AbbasiS #folksonomy #modelling #named: RichVSM: enRiched vector space models for folksonomies (RA, SS), pp. 219–228.
ICDAR-2009-AhmadVK #markov #recognition #using #word: Lexicon-Based Word Recognition Using Support Vector Machine and Hidden Markov Model (ARA, CVG, MK), pp. 161–165.
ICDAR-2009-NguyenTB #approach #visual notation: A Symbol Spotting Approach Based on the Vector Model and a Visual Vocabulary (TON, ST, AB), pp. 708–712.
ICDAR-2009-SidereHR #classification #graph #representation: Vector Representation of Graphs: Application to the Classification of Symbols and Letters (NS, PH, JYR), pp. 681–685.
ICDAR-2009-WakaharaU #2d #composition #using: Hierarchical Decomposition of Handwriting Deformation Vector Field Using 2D Warping and Global/Local Affine Transformation (TW, SU), pp. 1141–1145.
VLDB-2009-WillhalmPBPZS #in memory #named #performance #using: SIMD-Scan: Ultra Fast in-Memory Table Scan using on-Chip Vector Processing Units (TW, NP, YB, HP, AZ, JS), pp. 385–394.
TACAS-2009-BrummayerB #array #named #performance #smt: Boolector: An Efficient SMT Solver for Bit-Vectors and Arrays (RB, AB), pp. 174–177.
ICPC-2009-GrantC #analysis: Vector space analysis of software clones (SG, JRC), pp. 233–237.
STOC-2009-Peikert #problem #worst-case: Public-key cryptosystems from the worst-case shortest vector problem: extended abstract (CP), pp. 333–342.
HIMI-II-2009-MarcoMFG #effectiveness #image #performance: Evaluating the Effectiveness and the Efficiency of a Vector Image Search Tool (PDM, TDM, DF, MG), pp. 259–268.
ICEIS-J-2009-BohmHPLW #integration #process: Vectorizing Instance-Based Integration Processes (MB, DH, SP, WL, UW), pp. 40–52.
CIKM-2009-LiuH #evaluation #incremental #query: Incremental query evaluation for support vector machines (DL, KAH), pp. 1815–1818.
ECIR-2009-DemartiniGN #ranking: A Vector Space Model for Ranking Entities and Its Application to Expert Search (GD, JG, WN), pp. 189–201.
ICML-2009-DaitchKS #graph: Fitting a graph to vector data (SID, JAK, DAS), pp. 201–208.
ICML-2009-ZhangKP #learning #prototype #scalability: Prototype vector machine for large scale semi-supervised learning (KZ, JTK, BP), pp. 1233–1240.
MLDM-2009-BarakatB: The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines (NHB, APB), pp. 311–321.
MLDM-2009-FersiniMAA #approach #multi #recognition: Audio-Based Emotion Recognition in Judicial Domain: A Multilayer Support Vector Machines Approach (EF, EM, GA, FA), pp. 594–602.
MLDM-2009-KurasovaM #visualisation: Combination of Vector Quantization and Visualization (OK, AM), pp. 29–43.
MLDM-2009-RiesenB #difference #graph #prototype #reduction #using: Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes (KR, HB), pp. 617–631.
MLDM-2009-SegataB #dataset #performance #scalability: Fast Local Support Vector Machines for Large Datasets (NS, EB), pp. 295–310.
RE-2009-LongLYJ #approach #evaluation #requirements #security: AVT Vector: A Quantitative Security Requirements Evaluation Approach Based on Assets, Vulnerabilities and Trustworthiness of Environment (TL, LL, YY, ZJ), pp. 377–378.
SAC-2009-LinfordS #effectiveness #parallel: Vector stream processing for effective application of heterogeneous parallelism (JCL, AS), pp. 976–980.
SAC-2009-WangCH #learning #multi #music #retrieval: Music retrieval based on a multi-samples selection strategy for support vector machine active learning (TW, GC, PH), pp. 1750–1751.
CAV-2009-JhaLS #named #performance #smt: Beaver: Engineering an Efficient SMT Solver for Bit-Vector Arithmetic (SJ, RL, SAS), pp. 668–674.
ICST-2009-HalfondCO #identification #testing: Penetration Testing with Improved Input Vector Identification (WGJH, SRC, AO), pp. 346–355.
LICS-2009-Leroux #induction #invariant #problem #reachability: The General Vector Addition System Reachability Problem by Presburger Inductive Invariants (JL), pp. 4–13.
VMCAI-2009-Maier: Deciding Extensions of the Theories of Vectors and Bags (PM), pp. 245–259.
DAC-2008-AlkabaniMKP #variability: Input vector control for post-silicon leakage current minimization in the presence of manufacturing variability (YA, TM, FK, MP), pp. 606–609.
DAC-2008-GuzeyWLF #analysis #functional #testing: Functional test selection based on unsupervised support vector analysis (OG, LCW, JRL, HF), pp. 262–267.
DAC-2008-PaikS #multi #optimisation #standard: Multiobjective optimization of sleep vector for zigzag power-gated circuits in standard cell elements (SP, YS), pp. 600–605.
DATE-2008-KumarB: Vectorization of Reed Solomon Decoding and Mapping on the EVP (AK, KvB), pp. 450–455.
DATE-2008-ShahidiG #multi #testing: Multi-Vector Tests: A Path to Perfect Error-Rate Testing (SS, SG), pp. 1178–1183.
DRR-2008-ZouLT #html #online #using: Extracting a sparsely located named entity from online HTML medical articles using support vector machine (JZ, DXL, GRT), p. 68150.
STOC-2008-GamaN #difference: Finding short lattice vectors within mordell’s inequality (NG, PQN), pp. 207–216.
ECIR-2008-EidoonYO #ontology #using: Ontology Matching Using Vector Space (ZE, NY, FO), pp. 472–481.
ICML-2008-CatanzaroSK #classification #performance: Fast support vector machine training and classification on graphics processors (BCC, NS, KK), pp. 104–111.
ICML-2008-FrancLM #fault: Stopping conditions for exact computation of leave-one-out error in support vector machines (VF, PL, KRM), pp. 328–335.
ICML-2008-FrancS #algorithm: Optimized cutting plane algorithm for support vector machines (VF, SS), pp. 320–327.
ICML-2008-TakedaS: nu-support vector machine as conditional value-at-risk minimization (AT, MS), pp. 1056–1063.
ICPR-2008-ChangFLI #clustering #detection #kernel #multi: Clustered Microcalcification detection based on a Multiple Kernel Support Vector Machine with Grouped Features (GF-SVM) (TTC, JF, HWL, HHSI), pp. 1–4.
ICPR-2008-EngelC #invariant: Scale-invariant medial features based on gradient vector flow fields (DE, CC), pp. 1–4.
ICPR-2008-GeorgeKK #classification #hybrid: Hybrid wavelet support vector classification of temporal bone abnormalities (JG, STK, RK), pp. 1–4.
ICPR-2008-JiangDX #design: An affinity propagation based method for vector quantization codebook design (WJ, FD, QLX), pp. 1–4.
ICPR-2008-PorroHTNDB #evaluation #performance: Performance evaluation of relevance vector machines as a nonlinear regression method in real-world chemical spectroscopic data (DP, NHG, ITB, ON, AD, RJB), pp. 1–4.
ICPR-2008-SchenkR #online #recognition: Neural net vector quantizers for discrete HMM-based on-line handwritten whiteboard-note recognition (JS, GR), pp. 1–4.
ICPR-2008-TavakkoliNBN #incremental #modelling #performance: Efficient background modeling through incremental Support Vector Data Description (AT, MN, GB, MNN), pp. 1–4.
ICPR-2008-WangWW: Harris feature vector descriptor (HFVD) (XW, FCW, ZW), pp. 1–4.
ICPR-2008-XuD: A local descriptor for finding corresponding points in vector fields (LX, HQD), pp. 1–4.
ICPR-2008-XuP #image: Finding the splitting vector for image resolution up-conversion (XX, HP), pp. 1–4.
KDD-2008-Christen #automation #classification #nearest neighbour #using: Automatic record linkage using seeded nearest neighbour and support vector machine classification (PC), pp. 151–159.
KDD-2008-WuLCC #learning #symmetry: Asymmetric support vector machines: low false-positive learning under the user tolerance (SHW, KPL, CMC, MSC), pp. 749–757.
SIGIR-2008-ZhangL #learning #multi: Learning with support vector machines for query-by-multiple-examples (DZ, WSL), pp. 835–836.
SAC-2008-BragaOM #estimation #feature model #optimisation #parametricity: A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation (PLB, ALIO, SRLM), pp. 1788–1792.
SAC-2008-DiamantiniP #detection: Borderline detection by Bayes vector quantizers (CD, DP), pp. 904–908.
SAC-2008-MengleG #algorithm #ambiguity #classification #feature model #using: Using ambiguity measure feature selection algorithm for support vector machine classifier (SSRM, NG), pp. 916–920.
SAC-2008-SchreckFK #automation #multi #optimisation #towards: Towards automatic feature vector optimization for multimedia applications (TS, DWF, DAK), pp. 1197–1201.
SAC-2008-TakanoC #documentation #feature model #feedback: A light-weight feedback method for reconstructing a document vector space on a feature extraction model (KT, XC), pp. 1169–1170.
CC-2008-FranchettiP #generative #permutation: Generating SIMD Vectorized Permutations (FF, MP), pp. 116–131.
CGO-2008-HamptonA #architecture #compilation #thread: Compiling for vector-thread architectures (MH, KA), pp. 205–215.
DATE-2007-HuVKCP #dependence #estimation #memory management #performance: Fast memory footprint estimation based on maximal dependency vector calculation (QH, AV, PGK, FC, MP), pp. 379–384.
DATE-2007-JayakumarK #algorithm: An algorithm to minimize leakage through simultaneous input vector control and circuit modification (NJ, SPK), pp. 618–623.
DATE-2007-WangLHLYX #modelling #performance: Temperature-aware NBTI modeling and the impact of input vector control on performance degradation (YW, HL, KH, RL, HY, YX), pp. 546–551.
DRR-2007-KimLT #documentation #identification #online #using: Identification of comment-on sentences in online biomedical documents using support vector machines (ICK, DXL, GRT).
ICDAR-2007-CaoG #modelling #retrieval: Vector Model Based Indexing and Retrieval of Handwritten Medical Forms (HC, VG), pp. 88–92.
ICDAR-2007-ChellapillaP #retrieval #robust: Redundant Bit Vectors for Robust Indexing and Retrieval of Electronic Ink (KC, JCP), pp. 387–391.
ICDAR-2007-HuangLT #image #visual notation: Extraction of Vectorized Graphical Information from Scientific Chart Images (WH, RL, CLT), pp. 521–525.
ICDAR-2007-KeshariW #hybrid #recognition #using: Hybrid Mathematical Symbol Recognition Using Support Vector Machines (BK, SMW), pp. 859–863.
ICDAR-2007-NguyenBML #classification #using #verification: Off-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machines (VN, MB, VM, GL), pp. 734–738.
ICDAR-2007-SchneiderBR #documentation #robust: Robust Document Warping with Interpolated Vector Fields (DS, MB, RR), pp. 113–117.
TACAS-2007-BryantKOSSB #abstraction: Deciding Bit-Vector Arithmetic with Abstraction (REB, DK, JO, SAS, OS, BAB), pp. 358–372.
STOC-2007-HavivR #polynomial #problem: Tensor-based hardness of the shortest vector problem to within almost polynomial factors (IH, OR), pp. 469–477.
ICALP-2007-BlomerN: Sampling Methods for Shortest Vectors, Closest Vectors and Successive Minima (JB, SN), pp. 65–77.
ICALP-2007-IshaiMSW #approximate #multi: Private Multiparty Sampling and Approximation of Vector Combinations (YI, TM, MJS, RNW), pp. 243–254.
HCI-IPT-2007-HeBPC #mobile: Accelerated Rendering of Vector Graphics on Mobile Devices (GH, BB, ZP, XC), pp. 298–305.
HCI-MIE-2007-MascioFT #evaluation #image: Evaluation of VISTO: A New Vector Image Search TOol (TDM, DF, LT), pp. 836–845.
HIMI-MTT-2007-MascioLM #image #interface: The Interface of VISTO, a New Vector Image Search Tool (TDM, LL, VM), pp. 417–426.
HIMI-MTT-2007-XuLSB: A Method for Rule Extraction by Discernible Vector (EX, LS, ST, BY), pp. 779–784.
ICEIS-AIDSS-2007-LiuCM #recognition #using: Named Entity Recognition in Biomedical Literature Using Two-Layer Support Vector Machines (FL, YC, BM), pp. 39–48.
ICEIS-J-2007-LiuCM07a #comparison #random #recognition: Named Entity Recognition in Biomedical Literature: A Comparison of Support Vector Machines and Conditional Random Fields (FL, YC, BM), pp. 137–147.
CIKM-2007-LiMGDBM #predict #process #using: Predicting individual priorities of shared activities using support vector machines (LL, MJM, WG, CD, BB, DRM), pp. 515–524.
ECIR-2007-TzitzikasT: Naming Functions for the Vector Space Model (YT, YT), pp. 613–620.
ICML-2007-AsharafMS #multi: Multiclass core vector machine (SA, MNM, SKS), pp. 41–48.
ICML-2007-BordesBGW #multi: Solving multiclass support vector machines with LaRank (AB, LB, PG, JW), pp. 89–96.
ICML-2007-TsangKK: Simpler core vector machines with enclosing balls (IWT, AK, JTK), pp. 911–918.
ICML-2007-WangYL #algorithm #kernel: A kernel path algorithm for support vector machines (GW, DYY, FHL), pp. 951–958.
ICML-2007-WangZZ #array #classification #hybrid: Hybrid huberized support vector machines for microarray classification (LW, JZ, HZ), pp. 983–990.
ICML-2007-WipfN #using: Beamforming using the relevance vector machine (DPW, SSN), pp. 1023–1030.
ICML-2007-ZienBS: Transductive support vector machines for structured variables (AZ, UB, TS), pp. 1183–1190.
MLDM-2007-ChengCJY #feature model: Nonlinear Feature Selection by Relevance Feature Vector Machine (HC, HC, GJ, KY), pp. 144–159.
MLDM-2007-CzarnowskiJ #approach #multi: An Agent-Based Approach to the Multiple-Objective Selection of Reference Vectors (IC, PJ), pp. 117–130.
MLDM-2007-MartinsSSPG #classification #image #using: Classification of Breast Masses in Mammogram Images Using Ripley’s K Function and Support Vector Machine (LdOM, ECdS, ACS, ACdP, MG), pp. 784–794.
MLDM-2007-MendezCGRD #performance: Analyzing the Performance of Spam Filtering Methods When Dimensionality of Input Vector Changes (JRM, BC, DGP, FFR, FD), pp. 364–378.
MLDM-2007-RiesenKB #graph: Reducing the Dimensionality of Vector Space Embeddings of Graphs (KR, VK, HB), pp. 563–573.
MLDM-2007-SunV #using: Data Selection Using SASH Trees for Support Vector Machines (CS, RV), pp. 286–295.
MLDM-2007-ValinciusVBG #evolution: Evolving Committees of Support Vector Machines (DV, AV, MB, AG), pp. 263–275.
MLDM-2007-WuW #graph #kernel #parametricity: Choosing the Kernel Parameters for the Directed Acyclic Graph Support Vector Machines (KPW, SDW), pp. 276–285.
SIGIR-2007-RaghavanA #algorithm #feedback #interactive: An interactive algorithm for asking and incorporating feature feedback into support vector machines (HR, JA), pp. 79–86.
SIGIR-2007-YueFRJ #optimisation #precise: A support vector method for optimizing average precision (YY, TF, FR, TJ), pp. 271–278.
SAC-2007-KolesnikovA: Distortion-constrained compression of vector maps (AK, AA), pp. 8–12.
CGO-2007-BirkbeckLA #abstraction #approach #matlab: A Dimension Abstraction Approach to Vectorization in Matlab (NB, JL, JNA), pp. 115–130.
CAV-2007-GaneshD #array: A Decision Procedure for Bit-Vectors and Arrays (VG, DLD), pp. 519–531.
DAC-2006-ChengDCW #algorithm #generative #performance #power management #reduction: A fast simultaneous input vector generation and gate replacement algorithm for leakage power reduction (LC, LD, DC, MDFW), pp. 117–120.
DAC-2006-SinghMPO #nondeterminism #runtime: Gain-based technology mapping for minimum runtime leakage under input vector uncertainty (AKS, MM, RP, MO), pp. 522–527.
DATE-DF-2006-MadingLPSBEH #architecture #fixpoint: The vector fixed point unit of the synergistic processor element of the cell architecture processor (NM, JL, JP, RS, SB, SE, WH), pp. 244–248.
TACAS-2006-KroeningS #approximate #image #logic: Approximating Predicate Images for Bit-Vector Logic (DK, NS), pp. 242–256.
CSMR-2006-KaczorGH #algorithm #design pattern #identification #performance: Efficient Identification of Design Patterns with Bit-vector Algorithm (OK, YGG, SH), pp. 175–184.
PLDI-2006-NuzmanRZ: Auto-vectorization of interleaved data for SIMD (DN, IR, AZ), pp. 132–143.
SAS-2006-AmiranoffCF #abstract domain #relational: Beyond Iteration Vectors: Instancewise Relational Abstract Domains (PA, AC, PF), pp. 161–180.
IFL-2006-BerneckyHSTGS: Index Vector Elimination — Making Index Vectors Affordable (RB, SH, SBS, KT, CG, AVS), pp. 19–36.
CIKM-2006-Melucci #ranking #using: Ranking in context using vector spaces (MM), pp. 866–867.
CIKM-2006-OrdonezG #matrix #relational: Vector and matrix operations programmed with UDFs in a relational DBMS (CO, JGG), pp. 503–512.
CIKM-2006-PuppinS #documentation: The query-vector document model (DP, FS), pp. 880–881.
ICML-2006-LeSG #knowledge-based: Simpler knowledge-based support vector machines (QVL, AJS, TG), pp. 521–528.
ICML-2006-PereiraG #composition: The support vector decomposition machine (FP, GJG), pp. 689–696.
ICML-2006-TangM #multi: Multiclass reduced-set support vector machines (BT, DM), pp. 921–928.
ICML-2006-WangYL #2d: Two-dimensional solution path for support vector regression (GW, DYY, FHL), pp. 993–1000.
ICPR-v1-2006-AsharafM #clustering #scalability #using: Scalable non-linear Support Vector Machine using hierarchical clustering (SA, MNM), pp. 908–911.
ICPR-v1-2006-BhattacharyaRD #clustering #fuzzy #image #representation #retrieval #semantics #using: Image Representation and Retrieval Using Support Vector Machine and Fuzzy C-means Clustering Based Semantical Spaces (PB, MMR, BCD), pp. 929–935.
ICPR-v1-2006-QinL #algorithm: An Improved Semi-Supervised Support Vector Machine Based Translation Algorithm for BCI Systems (JQ, YL), pp. 1240–1243.
ICPR-v1-2006-ZhuJB #estimation: Nonlinear Eye Gaze Mapping Function Estimation via Support Vector Regression (ZZ, QJ, KPB), pp. 1132–1135.
ICPR-v2-2006-BarakatB #using: Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve (NHB, APB), pp. 812–815.
ICPR-v2-2006-BhattacharyaRD06a #clustering #fuzzy #image #representation #retrieval #semantics #using: Image Representation and Retrieval Using Support Vector Machine and Fuzzy C-means Clustering Based Semantical Spaces (PB, MMR, BCD), pp. 1162–1168.
ICPR-v2-2006-ChenB06a #kernel: Function Dot Product Kernels for Support Vector Machine (GC, PB), pp. 614–617.
ICPR-v2-2006-ChiangK #classification #using: Classification of Line and Character Pixels on Raster Maps Using Discrete Cosine Transformation Coefficients and Support Vector Machine (YYC, CAK), pp. 1034–1037.
ICPR-v2-2006-LiangZ #feature model #linear: Feature selection for linear support vector machines (ZL, TZ), pp. 606–609.
ICPR-v2-2006-StefanoDMF #learning: Improving Dynamic Learning Vector Quantization (CDS, CD, AM, ASdF), pp. 804–807.
ICPR-v2-2006-YeSLC #kernel #orthogonal: Support vector machine with orthogonal Chebyshev kernel (NY, RS, YL, LC), pp. 752–755.
ICPR-v2-2006-ZhengT #algorithm #robust: A Robust Algorithm for Generalized Orthonormal Discriminant Vectors (WZ, XT), pp. 784–787.
ICPR-v3-2006-ArreolaFB #classification #linear #performance #using: Fast Support Vector Machine Classification using linear SVMs (KZA, JF, HB), pp. 366–369.
ICPR-v3-2006-GinnekenM #image #nearest neighbour: Image Denoising with k-nearest Neighbor and Support Vector Regression (BvG, AM), pp. 603–606.
ICPR-v3-2006-GuoL #image #multi #using: Multi-modality Image Registration Using Mutual Information Based on Gradient Vector Flow (YG, CCL), pp. 697–700.
ICPR-v3-2006-LeePL #image #kernel #re-engineering: Face Reconstruction with Low Resolution Facial Images by Feature Vector Projection in Kernel Space (SWL, JP, SWL), pp. 1179–1182.
ICPR-v3-2006-MakkapatiM06a #using: Vector Quantization Using Reflections of Triangular Subcodevectors (VM, PRM), pp. 701–704.
ICPR-v3-2006-NagasakiMKS #adaptation #classification #image: A Coupon Classification Method Based on Adaptive Image Vector Matching (TN, KM, TK, HS), pp. 280–283.
ICPR-v3-2006-PuDJ #robust #towards: Toward Blind Robust Watermarking of Vector Maps (YCP, WCD, ICJ), pp. 930–933.
ICPR-v3-2006-VidholmSN #3d: Accelerating the Computation of 3D Gradient Vector Flow Fields (EV, PS, IN), pp. 677–680.
ICPR-v4-2006-KropotovPVV #kernel #on the #principle #using: On Kernel Selection in Relevance Vector Machines Using Stability Principle (DK, NP, OV, DV), pp. 233–236.
ICPR-v4-2006-KrugerSKAW #recognition #speech: Mixture of Support Vector Machines for HMM based Speech Recognition (SEK, MS, MK, EA, AW), pp. 326–329.
ICPR-v4-2006-PhamS #approximate #classification #clustering #metric #performance: Metric tree partitioning and Taylor approximation for fast support vector classification (TVP, AWMS), pp. 132–135.
ICPR-v4-2006-WangWLJK #analysis #classification #component #independence #using: Content-Based Audio Classification Using Support Vector Machines and Independent Component Analysis (JCW, JFW, CBL, KTJ, WHK), pp. 157–160.
KDD-2006-LaurLM #encryption: Cryptographically private support vector machines (SL, HL, TM), pp. 618–624.
KDD-2006-WuCCH #approximate #incremental #matrix: Incremental approximate matrix factorization for speeding up support vector machines (GW, EYC, YKC, CJH), pp. 760–766.
SEKE-2006-SornilS #detection #using: Improving Intrusion Detection Systems Using Reference Vectors (OS, PS), pp. 398–403.
SIGIR-2006-MaddageLK #music #retrieval: Music structure based vector space retrieval (NCM, HL, MSK), pp. 67–74.
CGO-2006-NuzmanH #multi: Multi-platform Auto-vectorization (DN, RH), pp. 281–294.
HPCA-2006-SubramaniamL #dependence #memory management #predict #scalability #scheduling: Store vectors for scalable memory dependence prediction and scheduling (SS, GHL), pp. 65–76.
CASE-2005-LiuHL #flexibility #scheduling #using: Dynamic scheduling of flexible manufacturing system using support vector machines (YHL, HPH, YSL), pp. 387–392.
ICDAR-2005-LeiG #classification #sequence: Similarity-driven Sequence Classification Based on Support Vector Machines (HL, VG), pp. 252–261.
ICDAR-2005-LiuLCL #performance #recognition: Fast Vector Matching Methods and Their Applications to Handwriting Recognition (YHL, WHL, FC, CCL), pp. 871–877.
ICDAR-2005-WangC #classification #using: A Hierarchical Classifier Using New Support Vector Machine (YCFW, DC), pp. 851–855.
VLDB-2005-GodfreySG #scalability #set: Maximal Vector Computation in Large Data Sets (PG, RS, JG), pp. 229–240.
VLDB-2005-MilenovaYC #database: SVM in Oracle Database 10g: Removing the Barriers to Widespread Adoption of Support Vector Machines (BLM, JY, MMC), pp. 1152–1163.
ICALP-2005-FranceschiniG #sorting: Optimal In-place Sorting of Vectors and Records (GF, RG), pp. 90–102.
ICEIS-v2-2005-KianmehrZNOA #approach #data mining #mining #network: Combining Neural Network and Support Vector Machine into Integrated Approach for Biodata Mining (KK, HZ, KN, TÖ, RA), pp. 182–187.
ICEIS-v5-2005-DixitM #classification #documentation #using: Electronic Document Classification Using Support Vector Machine — An Application for E-Learning (SD, LKM), pp. 191–198.
CIKM-2005-Melucci #modelling #using: Context modeling and discovery using vector space bases (MM), pp. 808–815.
ECIR-2005-KakadeR #encoding #xml: Encoding XML in Vector Spaces (VK, PR), pp. 96–111.
ICML-2005-ChuK: New approaches to support vector ordinal regression (WC, SSK), pp. 145–152.
ICML-2005-FinleyJ #clustering: Supervised clustering with support vector machines (TF, TJ), pp. 217–224.
ICML-2005-HillD #adaptation #classification #problem: Adapting two-class support vector classification methods to many class problems (SIH, AD), pp. 313–320.
ICML-2005-Joachims #metric #multi #performance: A support vector method for multivariate performance measures (TJ), pp. 377–384.
ICML-2005-NguyenH #performance: An efficient method for simplifying support vector machines (DN, TBH), pp. 617–624.
ICML-2005-RasmussenQ: Healing the relevance vector machine through augmentation (CER, JQC), pp. 689–696.
ICML-2005-TsangKL #problem #scalability: Core Vector Regression for very large regression problems (IWT, JTK, KTL), pp. 912–919.
KDD-2005-FungSR #linear: Rule extraction from linear support vector machines (GF, SS, RBR), pp. 32–40.
KDD-2005-JakulinMDBZ #visualisation: Nomograms for visualizing support vector machines (AJ, MM, JD, IB, BZ), pp. 108–117.
MLDM-2005-KarrasMGO #mining: Improved MRI Mining by Integrating Support Vector Machine Priors in the Bayesian Restoration (DAK, BGM, DGD, DvO), pp. 325–333.
MLDM-2005-LaiST #image #recognition: Support Vector Machine Experiments for Road Recognition in High Resolution Images (JYL, AS, JT), pp. 426–436.
SAC-2005-BustosKS: A pivot-based index structure for combination of feature vectors (BB, DAK, TS), pp. 1180–1184.
SAC-2005-PandeyGM #algorithm #learning #probability #scheduling: Stochastic scheduling of active support vector learning algorithms (GP, HG, PM), pp. 38–42.
DAC-2004-ChopraV #algorithm #pseudo: Implicit pseudo boolean enumeration algorithms for input vector control (KC, SBKV), pp. 767–772.
DATE-v1-2004-KielyG #modelling #performance #using: Performance Modeling of Analog Integrated Circuits Using Least-Squares Support Vector Machines (TK, GGEG), pp. 448–453.
DRR-2004-PengHL #automation #clustering #component #image: Automatic content extraction of filled-form images based on clustering component block projection vectors (HP, XH, FL), pp. 204–214.
VLDB-2004-SingithamMR #trade-off: Efficiency-Quality Tradeoffs for Vector Score Aggregation (PKCS, MSM, PR), pp. 624–635.
PLDI-2004-EichenbergerWO #architecture #constraints: Vectorization for SIMD architectures with alignment constraints (AEE, PW, KO), pp. 82–93.
AdaEurope-2004-Gasperoni #ada: Vector Processing in Ada (FG), pp. 321–331.
CIKM-2004-CaiH #categorisation #documentation: Hierarchical document categorization with support vector machines (LC, TH), pp. 78–87.
ICML-2004-BrefeldS #learning: Co-EM support vector learning (UB, TS).
ICML-2004-DSouzaVS: The Bayesian backfitting relevance vector machine (AD, SV, SS).
ICML-2004-JinL #induction #robust: Robust feature induction for support vector machines (RJ, HL).
ICML-2004-LawrenceP #learning: Learning to learn with the informative vector machine (NDL, JCP).
ICML-2004-TsochantaridisHJA #machine learning: Support vector machine learning for interdependent and structured output spaces (IT, TH, TJ, YA).
ICML-2004-VuralD #multi: A hierarchical method for multi-class support vector machines (VV, JGD).
ICPR-v1-2004-CevikalpW #recognition #using: Face Recognition by Using Discriminative Common Vectors (HC, DMW), pp. 326–329.
ICPR-v1-2004-Chen #kernel #robust: M-Estimator based Robust Kernels for Support Vector Machines (JHC), pp. 168–171.
ICPR-v1-2004-GokcenJD #bound #learning: Comparing Optimal Bounding Ellipsoid and Support Vector Machine Active Learning (IG, DJ, JRD), pp. 172–175.
ICPR-v1-2004-Horikawa #classification #comparison #invariant #kernel: Comparison of Support Vector Machines with Autocorrelation Kernels for Invariant Texture Classification (YH), pp. 660–663.
ICPR-v1-2004-LebrunCC #reduction #using: SVM Training Time Reduction using Vector Quantization (GL, CC, HC), pp. 160–163.
ICPR-v1-2004-NgTTS: Adjacent Orientation Vector Based Fingerprint Minutiae Matching System (GSN, XT, XT, DS), pp. 528–531.
ICPR-v1-2004-PnevmatikakisP #comparison: Comparison of Eigenface-Based Feature Vectors under Different Impairments (AP, LP), pp. 296–299.
ICPR-v2-2004-BanerjeeKM #detection #using: Corner Detection Using Support Vector Machines (MB, MKK, PM), pp. 819–822.
ICPR-v2-2004-KimuraKK #image #multi #retrieval #similarity #using: Acceleration of Similarity-Based Partial Image Retrieval using Multistage Vector Quantization (AK, TK, KK), pp. 993–996.
ICPR-v2-2004-QiuH #image: Grey Scale Image Skeletonisation from Noise-Damped Vector Potential (HQ, ERH), pp. 839–842.
ICPR-v2-2004-Robles-KellyH: Vector Field Smoothing Via Heat Flow (ARK, ERH), pp. 94–97.
ICPR-v2-2004-ShihL #analysis #detection #using #video: Face Detection Using Discriminating Feature Analysis and Support Vector Machine in Video (PS, CL), pp. 407–410.
ICPR-v2-2004-Sidenbladh #detection: Detecting Human Motion with Support Vector Machines (HS), pp. 188–191.
ICPR-v2-2004-SunWTC #estimation #recognition #robust: Robust Direction Estimation of Gradient Vector Field for Iris Recognition (ZS, YW, TT, JC), pp. 783–786.
ICPR-v3-2004-CuiG #recognition: Support Vector Machines for Face Recognition with Two-layer Generated Virtual Data (GC, WG), pp. 570–573.
ICPR-v3-2004-DavidL #search-based #using: Signal Discrimination Using a Support Vector Machine for Genetic Syndrome Diagnosis (AD, BL), pp. 490–493.
ICPR-v3-2004-HoiL #feedback: Group-based Relevance Feedback with Support Vector Machine Ensembles (SCHH, MRL), pp. 874–877.
ICPR-v3-2004-Hotta #kernel #recognition #robust: Support Vector Machine with Local Summation Kernel for Robust Face Recognition (KH), pp. 482–485.
ICPR-v3-2004-JainH #classification #component #gender #independence: Integrating Independent Components and Support Vector Machines for Gender Classification (AJ, JH), pp. 558–561.
ICPR-v3-2004-PozdnoukhovB #classification #image #invariant #kernel: Tangent Vector Kernels for Invariant Image Classification with SVMs (AP, SB), pp. 486–489.
ICPR-v3-2004-ShiNGY #classification #learning: Critical Vector Learning to Construct RBF Classifiers (DS, GSN, JG, DSY), pp. 359–362.
ICPR-v4-2004-GoniEVC #algorithm #detection #eye tracking #robust: Robust Algorithm for Pupil-Glint Vector Detection in a Video-oculography Eyetracking System (SG, JE, AV, RC), pp. 941–944.
ICPR-v4-2004-ImbaultL #approach #optimisation #parametricity #probability: A Stochastic Optimization Approach for Parameter Tuning of Support Vector Machines (FI, KL), pp. 597–600.
ICPR-v4-2004-OrtizMG #image: Gaussian Noise Elimination in Colour Images by Vector-Connected Filters (FO, FTM, PG), pp. 807–810.
ICPR-v4-2004-ParkJZK #clustering #graph: Support Vector Clustering Combined with Spectral Graph Partitioning (JHP, XJ, HZ, RK), pp. 581–584.
ICPR-v4-2004-SialaCCB #detection: Moving Shadow Detection with Support Vector Domain Description in the Color Ratios Space (KS, MC, FC, OB), pp. 384–387.
ICPR-v4-2004-StefanoDM #approach #learning: A Dynamic Approach to Learning Vector Quantization (CDS, CD, AM), pp. 601–604.
ICPR-v4-2004-ZhangS #difference #metric: Discovery of the Tri-Edge Inequality with Binary Vector Dissimilarity Measures (BZ, SNS), pp. 669–672.
KDD-2004-TruongLB #dataset #learning #random #using: Learning a complex metabolomic dataset using random forests and support vector machines (YT, XL, CB), pp. 835–840.
KDD-2004-WuS #information management: Incorporating prior knowledge with weighted margin support vector machines (XW, RKS), pp. 326–333.
SIGIR-2004-GreevyS #using: Classifying racist texts using a support vector machine (EG, AFS), pp. 468–469.
SAC-2004-Al-SadiA #algorithm #fault tolerance #performance #using: Efficient fault-tolerant routing algorithm for OTIS-cube using unsafety vectors (JAS, AMA), pp. 1426–1430.
SAC-2004-KomuraNTAI #multi #visualisation: Multidimensional support vector machines for visualization of gene expression data (DK, HN, ST, HA, SI), pp. 175–179.
SAC-2004-SciascioDM #documentation #knowledge base #retrieval #scalability: A knowledge based system for content-based retrieval of Scalable Vector Graphics documents (EDS, FMD, MM), pp. 1040–1044.
CAV-2004-GoelB #abstraction #functional #model checking #order #simulation: Symbolic Simulation, Model Checking and Abstraction with Partially Ordered Boolean Functional Vectors (AG, REB), pp. 255–267.
LICS-2004-GrooteGS #automaton: Vector Addition Tree Automata (PdG, BG, SS), pp. 64–73.
DAC-2003-BernardinisJS #performance #representation: Support vector machines for analog circuit performance representation (FDB, MIJ, ALSV), pp. 964–969.
DAC-2003-YuH: Vector potential equivalent circuit based on PEEC inversion (HY, LH), pp. 718–723.
DATE-2003-GoelB #analysis #functional #reachability #set: Set Manipulation with Boolean Functional Vectors for Symbolic Reachability Analysis (AG, REB), pp. 10816–10821.
DATE-2003-OhKWS #architecture #feedback #using: Test Pattern Compression Using Prelude Vectors in Fan-Out Scan Chain with Feedback Architecture (NO, RK, TWW, JS), pp. 10110–10115.
DATE-2003-RaoO #design: Virtual Compression through Test Vector Stitching for Scan Based Designs (WR, AO), pp. 10104–10109.
DRR-2003-RahmanTA #heuristic #hybrid #web: Exploring a hybrid of support vector machines (SVMs) and a heuristic-based system in classifying web pages (AFRR, YT, HA), pp. 120–127.
DRR-2003-RossetZH: Boosting and support vector machines as optimal separators (SR, JZ, TH), pp. 1–7.
DRR-2003-ZhangS #difference #identification #metric: Binary vector dissimilarity measures for handwriting identification (BZ, SNS), pp. 28–38.
ICDAR-2003-ChaTS #algorithm #optimisation #recognition #search-based #similarity #using: Optimizing Binary Feature Vector Similarity Measure using Genetic Algorithm and Handwritten Character Recognition (SHC, CCT, SNS), pp. 662–665.
ICDAR-2003-GribovB: Vectorization with the Voronoi L-diagram (AG, EB), pp. 1015–1019.
ICDAR-2003-Hilaire #algorithm #evaluation #performance: A Matching Scheme to Enhance Performance Evaluation of Raster-to-Vector Conversion Algorithms (XH), pp. 629–633.
ICDAR-2003-ViglinoP #approach #automation: A Vector Approach for Automatic Interpretation of the French Cadatral Map (JMV, MPD), pp. 304–308.
SIGMOD-2003-KriegelBKPS #set #similarity #using: Using Sets of Feature Vectors for Similarity Search on Voxelized CAD Objects (HPK, SB, PK, MP, MS), pp. 587–598.
VLDB-2003-ZhouS #clustering #metric: Data Bubbles for Non-Vector Data: Speeding-up Hierarchical Clustering in Arbitrary Metric Spaces (JZ, JS), pp. 452–463.
STOC-2003-Ajtai #algorithm #approximate #behaviour #worst-case: The worst-case behavior of schnorr’s algorithm approximating the shortest nonzero vector in a lattice (MA), pp. 396–406.
ICEIS-v2-2003-Abdel-WahaabBHH #3d #invariant #network #recognition #using: Three-Dimensional Object Recognition Using Support Vector Machine Neural Network Based on Moment Invariant Features (MSAW, SFB, ASH, DMH), pp. 583–588.
ICEIS-v2-2003-DoP #algorithm #dataset #mining #scalability: Mining Very Large Datasets with Support Vector Machine Algorithms (TND, FP), pp. 140–147.
CIKM-2003-MayfieldMPP #using: Lattice-based tagging using support vector machines (JM, PM, CDP, CP), pp. 303–308.
CIKM-2003-ShanahanR #classification: Boosting support vector machines for text classification through parameter-free threshold relaxation (JGS, NR), pp. 247–254.
ECIR-2003-XuYTXW #classification #using: Representative Sampling for Text Classification Using Support Vector Machines (ZX, KY, VT, XX, JW), pp. 393–407.
ICML-2003-AltunTH #markov: Hidden Markov Support Vector Machines (YA, IT, TH), pp. 3–10.
ICML-2003-Brinker #learning: Incorporating Diversity in Active Learning with Support Vector Machines (KB), pp. 59–66.
ICML-2003-DeCosteM #approximate #classification #incremental #kernel #performance: Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support Vectors (DD, DM), pp. 115–122.
ICML-2003-KondorJ #kernel #set: A Kernel Between Sets of Vectors (RK, TJ), pp. 361–368.
ICML-2003-ValentiniD #bias: Low Bias Bagged Support Vector Machines (GV, TGD), pp. 752–759.
ICML-2003-WuS #optimisation: New í-Support Vector Machines and their Sequential Minimal Optimization (XW, RKS), pp. 824–831.
MLDM-2003-DongKS #optimisation #parallel #performance: A Fast Parallel Optimization for Training Support Vector Machine (JxD, AK, CYS), pp. 96–105.
MLDM-2003-Tortorella: A ROC-Based Reject Rule for Support Vector Machines (FT), pp. 106–120.
SIGIR-2003-EvansBH #optimisation #performance #robust: Optimizing term vectors for efficient and robust filtering (DAE, JB, DAH), pp. 451–452.
SIGIR-2003-ZhangL #classification #using: Question classification using support vector machines (DZ, WSL), pp. 26–32.
CAV-2003-BoigelotHJ #automaton #hybrid #using: Hybrid Acceleration Using Real Vector Automata (Extended Abstract) (BB, FH, SJ), pp. 193–205.
DATE-2002-LiuCG #identification: An Interval-Based Diagnosis Scheme for Identifying Failing Vectors in a Scan-BIST Environment (CL, KC, MG), pp. 382–386.
CIAA-J-2000-BergeronH02 #algorithm #approximate #string: Vector Algorithms for Approximate String Matching (AB, SH), pp. 53–66.
ICML-2002-DeCoste #classification #distance #geometry #kernel #performance: Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry (DD), pp. 99–106.
ICML-2002-Zhang #behaviour #consistency #statistics: Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond (TZ0), pp. 690–700.
ICPR-v1-2002-AiYX #approach #detection: A Subspace Approach to Face Detection with Support Vector Machines (HA, LY, GX), pp. 45–48.
ICPR-v1-2002-CoquinB #image: A New Method to Compute the Distortion Vector Field from Two Images (DC, PB), pp. 279–282.
ICPR-v1-2002-MaD #detection: Face Detection Based on Hierarchical Support Vector Machines (YM, XD), pp. 222–225.
ICPR-v1-2002-WollnyTK #analysis #segmentation: Segmentation of Vector Fields by Critical Point Analysis: Application to Brain Deformation (GW, MT, FK), pp. 524–527.
ICPR-v2-2002-AnconaCSD #complexity #detection #image #parametricity #runtime: Object Detection in Images: Run-Time Complexity and Parameter Selection of Support Vector Machines (NA, GC, ES, AD), pp. 426–429.
ICPR-v2-2002-FrancH #multi: Multi-Class Support Vector Machine (VF, VH), pp. 236–239.
ICPR-v2-2002-FrantiX #classification #using: Classification of Binary Vectors by Using ??SC-Distance (PF, MX), pp. 52–55.
ICPR-v2-2002-HaasdonkK #distance #kernel: Tangent Distance Kernels for Support Vector Machines (BH, DK), pp. 864–868.
ICPR-v2-2002-KimPJKB #classification #using: Pattern Classification Using Support Vector Machine Ensemble (HCK, SP, HMJ, DK, SYB), pp. 160–163.
ICPR-v2-2002-LiuHLM #analysis #kernel #recognition: Kernel-Based Optimized Feature Vectors Selection and Discriminant Analysis for Face Recognition (QL, RH, HL, SM), pp. 362–365.
ICPR-v2-2002-XiaoAX #optimisation #set: Pair-Wise Sequential Reduced Set for Optimization of Support Vector Machines (XX, HA, GX), pp. 860–863.
ICPR-v2-2002-YuB #image #segmentation #using: Image Segmentation Using Gradient Vector Diffusion and Region Merging (ZY, CLB), pp. 941–944.
ICPR-v3-2002-MaragoudakisKF #independence #performance #segmentation: Incorporating Conditional Independence Assumption with Support Vector Machines to Enhance Handwritten Character Segmentation Performance (MM, EK, NF), pp. 911–914.
ICPR-v3-2002-SahbiB #classification #detection: Coarse-to-Fine Support Vector Classifiers for Face Detection (HS, NB), pp. 359–362.
ICPR-v3-2002-SmolkaSPV #on the #performance: On the Fast Modification of the Vector Median Filter (BS, MS, KNP, ANV), pp. 931–934.
ICPR-v4-2002-XiL #detection #feature model #using: Face Detection and Facial Feature Extraction Using Support Vector Machines (DX, SWL), pp. 209–212.
KDD-2002-Agarwal: Shrinkage estimator generalizations of Proximal Support Vector Machines (DKA), pp. 173–182.
KDD-2002-GodboleSC #multi #scalability #using: Scaling multi-class support vector machines using inter-class confusion (SG, SS, SC), pp. 513–518.
ECOOP-2002-Filman #encoding: Polychotomic Encoding: A Better Quasi-Optimal Bit-Vector Encoding of Tree Hierarchies (REF), pp. 545–561.
SAC-2002-Al-SadiDO #algorithm #fault tolerance #probability: Probability vectors: a new fault-tolerant routing algorithm for k-ary n-cubes (JAS, KD, MOK), pp. 830–834.
DocEng-2001-ProbetsMEB: Vector graphics: from PostScript and Flash to SVG (SGP, JCM, DRE, DFB), pp. 135–143.
ICDAR-2001-AyatCRS #image #kernel #named #pattern matching #pattern recognition #recognition: KMOD — A New Support Vector Machine Kernel with Moderate Decreasing for Pattern Recognition. Application to Digit Image Recognition (NEA, MC, LR, CYS), p. 1215–?.
ICDAR-2001-LeedhamTY #identification #markov #using: Handwritten Country Name Identification Using Vector Quantisation and Hidden Markov Model (GL, WKT, WLY), pp. 685–688.
STOC-2001-AjtaiKS #algorithm #problem: A sieve algorithm for the shortest lattice vector problem (MA, RK, DS), pp. 601–610.
STOC-2001-MostefaouiRR #distributed: Conditions on input vectors for consensus solvability in asynchronous distributed systems (AM, SR, MR), pp. 153–162.
CIAA-2001-BergeronH #algorithm: Cascade Decompositions are Bit-Vector Algorithms (AB, SH), pp. 13–26.
ICML-2001-ChuKO #framework: A Unified Loss Function in Bayesian Framework for Support Vector Regression (WC, SSK, CJO), pp. 51–58.
ICML-2001-DruckerSG #feedback #using: Relevance Feedback using Support Vector Machines (HD, BS, DCG), pp. 122–129.
ICML-2001-GartnerF #classification #named: WBCsvm: Weighted Bayesian Classification based on Support Vector Machines (TG, PAF), pp. 154–161.
KDD-2001-Agrarwal #modelling #predict: Applications of generalized support vector machines to predictive modeling (NA), p. 6.
KDD-2001-CarageaCH #classification #using: Gaining insights into support vector machine pattern classifiers using projection-based tour methods (DC, DC, VH), pp. 251–256.
KDD-2001-FungM #classification: Proximal support vector machine classifiers (GF, OLM), pp. 77–86.
MLDM-2001-ImiyaI #detection #statistics: Statistics of Flow Vectors and Its Application to the Voting Method for the Detection of Flow Fields (AI, KI), pp. 293–306.
SIGIR-2001-GodaKTFC #optimisation #problem #query: Query Optimization for Vector Space Problems (KG, MK, TT, OF, AC), pp. 416–417.
SIGIR-2001-Joachims #classification #learning #statistics: A Statistical Learning Model of Text Classification for Support Vector Machines (TJ), pp. 128–136.
SIGIR-2001-VoKM #effectiveness #ranking #termination: Vector-Space Ranking with Effective Early Termination (VNA, OdK, AM), pp. 35–42.
ECOOP-2001-RaynaudT #encoding #performance #testing: A Quasi Optimal Bit-Vector Encoding of Tree Hierarchies. Application to Efficient Type Inclusion Tests (OR, ET), pp. 165–180.
SAC-2001-FernandesTMR #algorithm #problem #search-based #using: Using assortative mating in genetic algorithms for vector quantization problems (CMF, RT, CM, ACR), pp. 361–365.
DATE-2000-JantschB: Composite Signal Flow: A Computational Model Combining Events, Sampled Streams, and Vectors (AJ, PB), pp. 154–160.
DATE-2000-SaabHK #fault #generative #parametricity #simulation: Parametric Fault Simulation and Test Vector Generation (KS, NBH, BK), pp. 650–656.
STOC-2000-OlshevskyS #confluence #matrix: Matrix-vector product for confluent Cauchy-like matrices with application to confluent rational interpolation (VO, MAS), pp. 573–581.
ICALP-2000-Blomer: Closest Vectors, Successive Minima, and Dual HKZ-Bases of Lattices (JB), pp. 248–259.
CHI-2000-LeeSRI #named: HandSCAPE: a vectorizing tape measure for on-site measuring applications (JL, VS, SR, HI), pp. 137–144.
CIKM-2000-FerhatosmanogluTAA #approximate #set: Vector Approximation based Indexing for Non-uniform High Dimensional Data Sets (HF, ET, DA, AEA), pp. 202–209.
ICML-2000-JiangL #approximate #information retrieval: Approximate Dimension Equalization in Vector-based Information Retrieval (FJ, MLL), pp. 423–430.
ICML-2000-KlinkenbergJ #concept #detection: Detecting Concept Drift with Support Vector Machines (RK, TJ), pp. 487–494.
ICML-2000-SchohnC #learning #less is more: Less is More: Active Learning with Support Vector Machines (GS, DC), pp. 839–846.
ICML-2000-TeowL #kernel #parametricity: Selection of Support Vector Kernel Parameters for Improved Generalization (LNT, KFL), pp. 967–974.
ICML-2000-TongK #classification #learning: Support Vector Machine Active Learning with Application sto Text Classification (ST, DK), pp. 999–1006.
ICPR-v1-2000-BigorgneAD #image #invariant #retrieval: An Invariant Local Vector for Content-Based Image Retrieval (EB, CA, JD), pp. 5019–5022.
ICPR-v1-2000-LiuRC #geometry #using: Using Geometric Properties of Correspondence Vectors for the Registration of Free-Form Shapes (YL, MAR, DC), pp. 5011–5014.
ICPR-v1-2000-YangM #classification #gender #visual notation: Support Vector Machines for Visual Gender Classification (MHY, BM), pp. 5115–5118.
ICPR-v2-2000-Ben-HurSHV #clustering: A Support Vector Clustering Method (ABH, HTS, DH, VV), pp. 2724–2727.
ICPR-v2-2000-DehghanFAS #fuzzy #markov #modelling #recognition #using #word: Off-Line Unconstrained Farsi Handwritten Word Recognition Using Fuzzy Vector Quantization and Hidden Markov Word Models (MD, KF, MA, MS), pp. 2351–2354.
ICPR-v2-2000-HermesB #feature model: Feature Selection for Support Vector Machines (LH, JMB), pp. 2712–2715.
ICPR-v2-2000-KimHL #retrieval: Retrieval of the Top N Matches with Support Vector Machines (JJK, BWH, SWL), pp. 2716–2719.
ICPR-v2-2000-MalekTA #classification #fault: Effect of the Feature Vector Size on the Generalization Error: The Case of MLPNN and RBFNN Classifiers (JEM, RT, AMA), pp. 2630–2633.
ICPR-v2-2000-MitraMP #database #incremental #learning #scalability: Data Condensation in Large Databases by Incremental Learning with Support Vector Machines (PM, CAM, SKP), pp. 2708–2711.
ICPR-v2-2000-ParkerPR #recognition: Vector Templates for Symbol Recognition (JRP, JP, DR), pp. 2602–2605.
ICPR-v2-2000-PavlovMD #algorithm #using: Scaling-Up Support Vector Machines Using Boosting Algorithm (DP, JM, BD), pp. 2219–2222.
ICPR-v2-2000-TombreT #recognition: Vectorization in Graphics Recognition: To Thin or Not to Thin (KT, ST), pp. 2091–2096.
ICPR-v2-2000-ZhaoLX #recognition: Support Vector Machine and its Application in Handwritten Numeral Recognition (BZ, YL, SX), pp. 2720–2723.
ICPR-v3-2000-Magrin-ChagnolleauD #pattern matching #pattern recognition #recognition: Application of Vector Filtering to Pattern Recognition (IMC, GD), pp. 3437–3440.
ICPR-v3-2000-PelecanosMSC #modelling #verification: Vector Quantization Based Gaussian Modeling for Speaker Verification (JWP, SM, SS, VC), pp. 3298–3301.
ICPR-v3-2000-StavrianopoulouA: The Euler Feature Vector (AS, VA), pp. 7034–7036.
ICPR-v3-2000-YamanaSA #detection #image #reliability #using: Edge Plane Detection in Spatio-Temporal Images by Using Edge Vector and Edge Reliability (FY, TS, KA), pp. 3664–3667.
ICPR-v3-2000-ZouY #clustering #image: Line Image Vectorization Based on Shape Partitioning and Merging (JJZ, HY), pp. 7006–7009.
ICPR-v4-2000-AchardDL #image #retrieval: Object Image Retrieval with Image Compactness Vectors (CA, JD, LL), pp. 4271–4274.
ICPR-v4-2000-ColiosT #identification #invariant #permutation: Landmark Identification Based on Projective and Permutation Invariant Vectors (CIC, PET), pp. 4128–4131.
ICPR-v4-2000-NakajimaIPP #correlation #detection #invariant #recognition: Object Recognition and Detection by a Combination of Support Vector Machine and Rotation Invariant Phase Only Correlation (CN, NI, MP, TP), pp. 4787–4790.
ICPR-v4-2000-TerrillonSSFA #detection #invariant: Invariant Face Detection with Support Vector Machines (JCT, MNS, MS, HF, SA), pp. 4210–4217.
ICPR-v4-2000-YuT #documentation #retrieval: Image-Based Document Vectors for Text Retrieval (ZY, CLT), pp. 4393–4396.
KDD-2000-DeCosteW: Alpha seeding for support vector machines (DD, KW), pp. 345–349.
KDD-2000-FungM #classification: Data selection for support vector machine classifiers (GF, OLM), pp. 64–70.
KDD-2000-PavlovCS #scalability #towards #using: Towards scalable support vector machines using squashing (DP, DC, PS), pp. 295–299.
SIGIR-2000-PremingerD #documentation #interactive #named #retrieval #user interface #visual notation: Uexküll: an interactive visual user interface for document retrieval in vector space (MP, SD), p. 392.
SIGIR-2000-SoboroffN #collaboration: Collaborative filtering and the generalized vector space model (IS, CKN), pp. 351–353.
HPCA-2000-MathewMCD #design #memory management #parallel: Design of a Parallel Vector Access Unit for SDRAM Memory Systems (BKM, SAM, JBC, AD), pp. 39–48.
DAC-1999-FallahAD #generative #simulation: Simulation Vector Generation from HDL Descriptions for Observability-Enhanced Statement Coverage (FF, PA, SD), pp. 666–671.
DATE-1999-FerrandiFGS #functional #generative #specification: Symbolic Functional Vector Generation for VHDL Specifications (FF, FF, LG, DS), p. 442–?.
ICDAR-1999-GrabowskiLM #segmentation: Stepwise Segmentation and Interpretation of Section Representations in Vectorized Drawings (HG, CL, AM), pp. 677–680.
STOC-1999-BlomerS #complexity #independence #on the: On the Complexity of Computing Short Linearly Independent Vectors and Short Bases in a Lattice (JB, JPS), pp. 711–720.
ICML-1999-Joachims #classification #using: Transductive Inference for Text Classification using Support Vector Machines (TJ), pp. 200–209.
KDD-1999-SyedLS #case study #independence: A Study of Support Vectors on Model Independent Example Selection (NAS, HL, KKS), pp. 272–276.
KDD-1999-SyedLS99a #concept #incremental #learning: Handling Concept Drifts in Incremental Learning with Support Vector Machines (NAS, HL, KKS), pp. 317–321.
SIGIR-1999-TurpinM #information retrieval #statistics: Statistical Phrases for Vector-Space Information Retrieval (poster abstract) (AT, AM), pp. 309–310.
SAC-1999-BakC #protocol #random: Randomized Distance-Vector Routing Protocol (SB, JAC), pp. 78–84.
DAC-1998-BarrettDL: A Decision Procedure for Bit-Vector Arithmetic (CWB, DLD, JRL), pp. 522–527.
DAC-1998-FallahDK #functional #generative #linear #modelling #programming #satisfiability #using: Functional Vector Generation for HDL Models Using Linear Programming and 3-Satisfiability (FF, SD, KK), pp. 528–533.
DATE-1998-GuoPR #sequence #testing: Procedures for Static Compaction of Test Sequences for Synchronous Sequential Circuits Based on Vector Restoration (RG, IP, SMR), pp. 583–587.
DATE-1998-RiesgoTTU #estimation #fault #functional #modelling #quality #validation: Quality Estimation of Test Vectors and Functional Validation Procedures Based on Fault and Error Models (TR, YT, EdlT, JU), pp. 955–956.
TACAS-1998-BjornerP: Deiding Fixed and Non-fixed Size Bit-vectors (NB, MCP), pp. 376–392.
STOC-1998-Ajtai #np-hard #problem #random #reduction: The Shortest Vector Problem in L2 is NP-hard for Randomized Reductions (Extended Abstract) (MA), pp. 10–19.
ICML-1998-BradleyM #feature model: Feature Selection via Concave Minimization and Support Vector Machines (PSB, OLM), pp. 82–90.
ICML-1998-FriessCC #algorithm #kernel #learning #performance: The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines (TTF, NC, CC), pp. 188–196.
ICPR-1998-BischofL #design: MDL-based design of vector quantizers (HB, AL), pp. 891–893.
ICPR-1998-ChenS #coordination #image: Image coordinate transformation based on DIV-CURL vector splines (FC, DS), pp. 518–520.
ICPR-1998-HuangSW #using: Face pose discrimination using support vector machines (SVM) (JH, XS, HW), pp. 154–156.
ICPR-1998-Kwok #classification #problem: Support vector mixture for classification and regression problems (JTYK), pp. 255–258.
ICPR-1998-LeeCC #automation #information management #knowledge-based: A knowledge-based automated vectorizing system for geographic information system (KHL, SBC, YCC), pp. 1546–1548.
ICPR-1998-LuoCH #detection #using: Corner detection using vector potential (BL, ADJC, ERH), pp. 1018–1021.
ICPR-1998-SatoY #classification #learning #using: A formulation of learning vector quantization using a new misclassification measure (AS, KY), pp. 322–325.
ICPR-1998-SunOA #algorithm #multi #recognition #taxonomy: An algorithm for constructing a multi-template dictionary for character recognition considering distribution of feature vectors (FS, SO, HA), pp. 1114–1116.
ICPR-1998-ZhangFY98a #verification: Handwritten signature verification based on neural “gas” based vector quantization (BZ, MF, HY), pp. 1862–1864.
DAC-1997-KrsticC #generative: Vector Generation for Maximum Instantaneous Current Through Supply Lines for CMOS Circuits (AK, KTC), pp. 383–388.
EDTC-1997-SeongK #clustering #design #layout: Two-way partitioning based on direction vector [layout design] (KSS, CMK), pp. 306–310.
ICDAR-1997-DoriW #algorithm #segmentation: Arc Segmentation from Complex Line Environments: A Vector-Based Stepwise Recovery Algorithm (DD, LW), pp. 76–80.
ICDAR-1997-KaufmannBH #reduction: Lexicon Reduction in an HMM-Framework Based on Quantized Feature Vectors (GK, HB, MH), pp. 1097–1101.
ICDAR-1997-WaizumiKSN #classification #learning #using: High speed rough classification for handwritten characters using hierarchical learning vector quantization (YW, NK, KS, YN), pp. 23–27.
ICFP-1997-ChinH #bound: A Bounds Inference Method for Vector-Based Memoisation (WNC, MH), pp. 176–187.
CIKM-1997-LundquistGF #feedback: Improving Relevance Feedback in the Vector Space Model (CL, DAG, OF), pp. 16–23.
ICML-1997-Baxter #approximate #canonical: The Canonical Distortion Measure for Vector Quantization and Function Approximation (JB), pp. 39–47.
SAC-1997-GoliP #data mining #mining #multi: Application of domain vector perfect hash join for multimedia data mining (VNRG, WP), pp. 334–339.
HPCA-1997-EspasaV #architecture #parallel #thread: Multithreaded Vector Architectures (RE, MV), pp. 237–248.
CAV-1997-CyrlukMR #formal method #performance: An Efficient Decision Procedure for the Theory of Fixed-Sized Bit-Vectors (DC, MOM, HR), pp. 60–71.
DAC-1996-HuangCCL #generative #simulation: Compact Vector Generation for Accurate Power Simulation (SYH, KCC, KTC, TCL), pp. 161–164.
DAC-1996-JonesP #automation #design #functional #generative: The Automatic Generation of Functional Test Vectors for Rambus Designs (KDJ, JPP), pp. 415–420.
DAC-1996-TsuiMMP #performance: Improving the Efficiency of Power Simulators by Input Vector Compaction (CYT, RM, DM, MP), pp. 165–168.
CIKM-1996-AbdelguerfiCWSMBR #database #representation: A Terrain Database Representation Based on an Extended Vector Product Format (MA, EC, CW, KS, VM, RB, BR), pp. 27–33.
CIKM-1996-Henrich #adaptation #documentation: Adapting a Spatial Access Structure for Document Representations in Vector Space (AH), pp. 19–26.
ICML-1996-Burges: Simplified Support Vector Decision Rules (CJCB), pp. 71–77.
ICPR-1996-CucchiaraF #identification: The vector-gradient Hough transform for identifying straight-translation generated shapes (RC, FF), pp. 502–510.
ICPR-1996-LambertN #invariant #using: Discrimination properties of invariants using the line moments of vectorized contours (GL, JN), pp. 735–739.
ICPR-1996-LiuD #algorithm #performance: Sparse pixel tracking: a fast vectorization algorithm applied to engineering drawings (WL, DD), pp. 808–812.
ICPR-1996-MichaelisSSM #adaptation #network #using: Adaptive filtering of distorted displacement vector fields using artificial neural networks (BM, OS, US, RM), pp. 335–339.
ICPR-1996-MollerGW #clustering #network #performance: Fast vector quantizer on neural clustering networks providing globally optimal cluster solutions (UM, MG, HW), pp. 351–355.
ICPR-1996-SugiyamaA #analysis #multi: Edge feature analysis by a vectorized feature extractor and in multiple edges (TS, KA), pp. 280–284.
SIGIR-1996-HanM #automation #image #retrieval: Image Organization and Retrieval with Automatically Constructed Feature Vectors (KAH, SHM), pp. 157–165.
SAC-1996-HoeltingSW96a #algorithm #precedence #problem #search-based #using: A genetic algorithm for the minimum broadcast time problem using a global precedence vector (CJH, DAS, RLW), pp. 258–262.
HPCA-1996-EspasaV #architecture: Decoupled Vector Architectures (RE, MV), pp. 281–290.
HPCA-1996-YangSD #parallel #query: Parallel Intersecting Compressed Bit Vectors in a High Speed Query Server for Processing Postal Addresses (WjY, RS, VD), pp. 232–241.
ICDAR-v1-1995-DasL #integration #recognition #set: Recognition of dimension sets and integration with vectorized engineering drawings (AKD, NAL), pp. 347–350.
ICDAR-v1-1995-RoosliM #constraints #metric #quality: A high quality vectorization combining local quality measures and global constraints (MR, GM), pp. 243–248.
ICDAR-v2-1995-EikvilAK #interactive #tool support: Tools for interactive map conversion and vectorization (LE, KA, HK), pp. 927–930.
ICDAR-v2-1995-FanCW #approach #image: A new vectorization-based approach to the skeletonization of binary images (KCF, DFC, MGW), pp. 627–630.
ICDAR-v2-1995-SivaramakrishnanPHSH #classification #documentation #generative #using: Zone classification in a document using the method of feature vector generation (RS, ITP, JH, SS, RMH), pp. 541–544.
VLDB-1995-GravanoG #database: Generalizing GlOSS to Vector-Space Databases and Broker Hierarchies (LG, HGM), pp. 78–89.
DLT-1995-Satta #problem: The Membership Problem for Unordered Vector Languages (GS), pp. 267–275.
SIGIR-1995-EfraimidisGMST #parallel #performance #retrieval #using: Parallel Text Retrieval on a High Performance Super Computer Using the Vector Space Model (PSE, CG, BM, PGS, BT), pp. 58–66.
SAC-1995-PerrizoG #query: Domain vector hashing for earth system data querying (WP, VNRG), pp. 71–75.
HPCA-1995-Lee #memory management #order: Memory Access Reordering in Vector Processors (DLL), pp. 380–389.
DAC-1994-NagumoNNMM #fault #named #reduction #using: VFSIM: Vectorized Fault Simulator Using a Reduction Technique Excluding Temporarily Unobservable Faults (TN, MN, TN, MM, SM), pp. 510–515.
VLDB-1994-MedianoCD #named: V-Trees — A Storage Method for Long Vector Data (MRM, MAC, MD), pp. 321–330.
ICDAR-1993-Ho #independence #learning #recognition: Recognition of handwritten digits by combining independent learning vector quantizations (TKH), pp. 818–821.
ICDAR-1993-HoriT: Raster-to-vector conversion by line fitting based on contours and skeletons (OH, ST), pp. 353–358.
ICDAR-1993-TanakaKT #development #process: Development of a map vectorization method involving a shape reforming process (NT, TK, JT), pp. 680–683.
PPoPP-1993-PrinsP #source code: Transforming High-Level Data-Parallel Programs into Vector Operations (JP, DWP), pp. 119–128.
CAV-1993-Krishnakumar #composition #finite #reachability #state machine: Reachability and Recurrence in Extended Finite State Machines: Modular Vector Addition Systems (ASK), pp. 110–122.
DAC-1992-WakabayashiT #dependence #independence #scheduling: Global Scheduling Independent of Control Dependencies Based on Condition Vectors (KW, HT), pp. 112–115.
SIGIR-1992-WangWY #analysis #geometry #modelling: An Analysis of Vector Space Models Based on Computational Geometry (ZW, SKMW, YY), pp. 152–160.
ICSE-1992-PearceL #multi #specification: The Property Vector Specification of a Multiset Iterator (TWP, DAL), pp. 235–245.
DAC-1991-OchiIY: Breadth-First Manipulation of SBDD of Boolean Functions for Vector Processing (HO, NI, SY), pp. 413–416.
SIGIR-1991-Sutcliffe #distributed #information retrieval #using: Distributed Representations in a Text Based Information Retrieval System: A New Way of Using the Vector Space Model (RFES), pp. 123–132.
ASPLOS-1991-Mangione-SmithAD #design #scheduling: Vector Register Design for Polycyclic Vector Scheduling (WHMS, SGA, ESD), pp. 154–163.
CAV-1991-HiraishiHOY #logic #model checking #verification: Vectorized Symbolic Model Checking of Computation Tree Logic for Sequential Machine Verification (HH, KH, HO, SY), pp. 214–224.
SIGIR-1990-BooksteinK #graph: Construction of Optimal Graphs for Bit-Vector Compression (AB, STK), pp. 327–342.
CAV-1990-HiraishiMH #logic #model checking: Vectorized Model Checking for Computation Tree Logic (HH, SM, KH), pp. 44–53.
DAC-1989-SadayappanV #matrix #performance #simulation: Efficient Sparse Matrix Factorization for Circuit Simulation on Vector Supercomputers (PS, VV), pp. 13–18.
POPL-1989-BaxterB #dependence #graph: The Program Dependence Graph and Vectorization (WB, HRBI), pp. 1–11.
ASPLOS-1989-JouppiBW #architecture #float: A Unified Vector/Scalar Floating-Point Architecture (NPJ, JB, DWW), pp. 134–143.
DAC-1988-KazamaKNM #algorithm #evaluation #logic #performance #simulation: Algorithm for Vectorizing Logic Simulation and Evaluation of “VELVET” Performance (YK, YK, MN, HM), pp. 231–236.
PLDI-1988-AllenJ #c #compilation #parallel: Compiling C for Vectorization, Parallelization, and Inline Expansion (RA, SJ), pp. 241–249.
ML-1988-Segen88a #concept: Conceptual Clumping of Binary Vectors with Occam’s Razor (JS), pp. 47–53.
ICSE-1988-MathurK #modelling: Modeling Mutation on a Vector Processor (APM, EWK), pp. 154–161.
DAC-1987-ChandraP #approach #generative: A Hierarchical Approach Test Vector Generation (SJC, JHP), pp. 495–501.
DAC-1987-VladimirescuWKBKDNJL #hardware #simulation: A Vector Hardware Accelerator with Circuit Simulation Emphasis (AV, DW, MK, ZB, AK, KD, KCN, NJ, SL), pp. 89–94.
ICALP-1987-HowellR #equivalence #reachability: Completeness Rules for Reachability, Containment, and Equivalence, with Respect to Conflict-Free Vector Replacement Systems (RRH, LER), pp. 509–520.
SIGIR-1986-ChouekaFKS #documentation #retrieval: Improved Hierarchical Bit-Vector Compression in Document Retrieval Systems (YC, ASF, STK, ES), pp. 88–96.
SIGIR-1986-WongZRW #on the #query: On Extending the Vector Space Model for Boolean Query Processing (SKMW, WZ, VVR, PCNW), pp. 175–185.
STOC-1985-Huynh #commutative #complexity #equivalence #problem #symmetry: The Complexity of the Equivalence Problem for Commutative Semigroups and Symmetric Vector Addition Systems (DTH), pp. 405–412.
SIGIR-1985-BuckleyL #optimisation: Optimization of Inverted Vector Searches (CB, AFL), pp. 97–110.
SIGIR-1985-WongZW #information retrieval: Generalized Vector Space Model in Information Retrieval (SKMW, WZ, PCNW), pp. 18–25.
SIGIR-1984-WongR #information retrieval: Vector Space Model of Information Retrieval — A Reevaluation (SKMW, VVR), pp. 167–185.
STOC-1982-Kosaraju #decidability #reachability: Decidability of Reachability in Vector Addition Systems (Preliminary Version) (SRK), pp. 267–281.
SIGIR-1982-Bookstein #information retrieval #modelling: Explanation and Generalization of Vector Models in Information Retrieval (AB), pp. 118–132.
DAC-1981-Krohn #simulation: Vector coding techniques for high speed digital simulation (HEK), pp. 525–529.
DAC-1980-UlrichLPTKEB #concurrent #fault #performance #simulation: High-speed concurrent fault simulation with vectors and scalars (EU, DL, NP, JT, MK, TE, RB), pp. 374–380.
STOC-1980-Strong #execution #graph: Vector Execution of Flow Graphs (Extended Abstract) (HRS), pp. 108–116.
DAC-1978-Fortin #approximate #diagrams #named #using: BUBBLE: Relationship diagrams using iterative vector approximation (GF), pp. 145–151.
STOC-1977-SacerdoteT #decidability #problem #reachability: The Decidability of the Reachability Problem for Vector Addition Systems (Preliminary Version) (GSS, RLT), pp. 61–76.
STOC-1974-Leeuwen #problem: A Partial Solution to the Reachability-Problem for Vector-Addition Systems (JvL), pp. 303–309.
STOC-1974-PrattRS #power of: A Characterization of the Power of Vector Machines (VRP, MOR, LJS), pp. 122–134.