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Stem spectral$ (all stems)

174 papers:

STOCSTOC-2015-ZhuLO #matrix #multi
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates (ZAZ, ZL, LO), pp. 237–245.
ICMLICML-2015-BoutsidisKG #clustering
Spectral Clustering via the Power Method — Provably (CB, PK, AG), pp. 40–48.
ICMLICML-2015-ChenS #rank
Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons (YC, CS), pp. 371–380.
ICMLICML-2015-GhoshdastidarD #clustering
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning (DG, AD), pp. 400–409.
ICMLICML-2015-Yang0JZ #bound #fault #set
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection (TY, LZ, RJ, SZ), pp. 135–143.
KDDKDD-2015-GleichM #algorithm #graph #learning #using
Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms (DFG, MWM), pp. 359–368.
KDDKDD-2015-LiuLWTF #clustering
Spectral Ensemble Clustering (HL, TL, JW, DT, YF), pp. 715–724.
DATEDATE-2014-KarakonstantisSSAB #analysis #approach #energy #variability
A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability (GK, AS, MMS, DA, AB), pp. 1–6.
DRRDRR-2014-LiPLD #analysis #online #verification
On-line signature verification method by Laplacian spectral analysis and dynamic time warping (CL, LP, CL, XD), p. ?–10.
DLTDLT-2014-Sinya #automaton #finite #graph
Graph Spectral Properties of Deterministic Finite Automata — (Short Paper) (RS), pp. 76–83.
ICMLICML-c1-2014-DenisGH #bound #learning #matrix
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning (FD, MG, AH), pp. 449–457.
ICMLICML-c2-2014-GleichM #algorithm #approximate #case study
Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow (DG, MWM), pp. 1018–1025.
ICMLICML-c2-2014-QuattoniBCG #sequence
Spectral Regularization for Max-Margin Sequence Tagging (AQ, BB, XC, AG), pp. 1710–1718.
ICMLICML-c2-2014-ValkoMKK #graph
Spectral Bandits for Smooth Graph Functions (MV, RM, BK, TK), pp. 46–54.
ICPRICPR-2014-BruneauPO #algorithm #automation #clustering #heuristic
A Heuristic for the Automatic Parametrization of the Spectral Clustering Algorithm (PB, OP, BO), pp. 1313–1318.
ICPRICPR-2014-GuoZLCZ #clustering #kernel #learning #multi
Multiple Kernel Learning Based Multi-view Spectral Clustering (DG, JZ, XL, YC, CZ), pp. 3774–3779.
ICPRICPR-2014-HiraiOHT
An HDR Spectral Imaging System for Time-Varying Omnidirectional Scene (KH, NO, TH, ST), pp. 2059–2064.
ICPRICPR-2014-HuynhR #image
Recovery of Spectral Sensitivity Functions from a Colour Chart Image under Unknown Spectrally Smooth Illumination (CPH, ARK), pp. 708–713.
ICPRICPR-2014-IoannidisCL #clustering #modelling #multi #using
Key-Frame Extraction Using Weighted Multi-view Convex Mixture Models and Spectral Clustering (AI, VC, AL), pp. 3463–3468.
ICPRICPR-2014-JenzriFG #robust
Robust Context Dependent Spectral Unmixing (HJ, HF, PDG), pp. 643–647.
ICPRICPR-2014-KacheleZMS #feature model #quality #recognition #using
Prosodic, Spectral and Voice Quality Feature Selection Using a Long-Term Stopping Criterion for Audio-Based Emotion Recognition (MK, DZ, SM, FS), pp. 803–808.
ICPRICPR-2014-LefevreAG #clustering
Brain Lobes Revealed by Spectral Clustering (JL, GA, DG), pp. 562–567.
ICPRICPR-2014-NocetiO #classification #graph #kernel #process
A Spectral Graph Kernel and Its Application to Collective Activities Classification (NN, FO), pp. 3892–3897.
ICPRICPR-2014-Ozay #image #multi #segmentation
Semi-supervised Segmentation Fusion of Multi-spectral and Aerial Images (MO), pp. 3839–3844.
ICPRICPR-2014-TasdemirMY #approximate #clustering
Geodesic Based Similarities for Approximate Spectral Clustering (KT, YM, IY), pp. 1360–1364.
KDDKDD-2014-WangZQZ #using
Improving the modified nyström method using spectral shifting (SW, CZ, HQ, ZZ), pp. 611–620.
SACSAC-2014-QuilleTR #analysis
Spectral analysis and text processing over the computer science literature: patterns and discoveries (RVEQ, CTJ, JFRJ), pp. 653–657.
DATEDATE-2013-TodorovMRS #approach #clustering #synthesis
A spectral clustering approach to application-specific network-on-chip synthesis (VT, DMG, HR, US), pp. 1783–1788.
STOCSTOC-2013-KwokLLGT #algorithm #analysis #clustering #difference #higher-order
Improved Cheeger’s inequality: analysis of spectral partitioning algorithms through higher order spectral gap (TCK, LCL, YTL, SOG, LT), pp. 11–20.
STOCSTOC-2013-Miller #graph #optimisation #problem #scalability #using
Solving large optimization problems using spectral graph theory (GLM), p. 981.
DLTDLT-2013-Jungers
Joint Spectral Characteristics: A Tale of Three Disciplines (RMJ), pp. 27–28.
HCIHCI-III-2013-YangWC #clustering #fuzzy #image #kernel #segmentation #similarity
Kernel Fuzzy Similarity Measure-Based Spectral Clustering for Image Segmentation (YY, YW, YmC), pp. 246–253.
ICMLICML-c1-2013-BootsG #approach #learning
A Spectral Learning Approach to Range-Only SLAM (BB, GJG), pp. 19–26.
ICMLICML-c3-2013-ChagantyL #linear
Spectral Experts for Estimating Mixtures of Linear Regressions (ATC, PL), pp. 1040–1048.
ICMLICML-c3-2013-ChenC #matrix
Spectral Compressed Sensing via Structured Matrix Completion (YC, YC), pp. 414–422.
ICMLICML-c3-2013-HuangS #learning #markov #modelling
Spectral Learning of Hidden Markov Models from Dynamic and Static Data (TKH, JGS), pp. 630–638.
STOCSTOC-2012-LeeGT #clustering #higher-order #multi
Multi-way spectral partitioning and higher-order cheeger inequalities (JRL, SOG, LT), pp. 1117–1130.
STOCSTOC-2012-OrecchiaSV #algorithm #approximate #exponential
Approximating the exponential, the lanczos method and an Õ(m)-time spectral algorithm for balanced separator (LO, SS, NKV), pp. 1141–1160.
CIKMCIKM-2012-ShangJLW #learning
Learning spectral embedding via iterative eigenvalue thresholding (FS, LCJ, YL, FW), pp. 1507–1511.
ICMLICML-2012-BalleQC #learning #modelling #optimisation
Local Loss Optimization in Operator Models: A New Insight into Spectral Learning (BB, AQ, XC), p. 236.
ICMLICML-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.
ICPRICPR-2012-FuLBF
Spectral correspondence method for fingerprint minutia matching (XF, CL, JB, JF), pp. 1743–1746.
ICPRICPR-2012-GuiST #analysis #estimation #parametricity
Regularization parameter estimation for spectral regression discriminant analysis based on perturbation theory (JG, ZS, TT), pp. 401–404.
ICPRICPR-2012-HuZFZ #clustering #multi #strict
Multi-way constrained spectral clustering by nonnegative restriction (HH, JZ, JF, JZ), pp. 1550–1553.
ICPRICPR-2012-LamSS #image #statistics #using
Denoising hyperspectral images using spectral domain statistics (AL, IS, YS), pp. 477–480.
ICPRICPR-2012-MitraKGSMLOVM #clustering #multimodal #performance
Spectral clustering to model deformations for fast multimodal prostate registration (JM, ZK, SG, DS, RM, XL, AO, JCV, FM), pp. 2622–2625.
ICPRICPR-2012-PanS #3d
3D shape isometric correspondence by spectral assignment (XP, LGS), pp. 2210–2213.
ICPRICPR-2012-RatnasingamR #recognition #representation
A spectral reflectance representation for recognition and reproduction (SR, ARK), pp. 1900–1903.
ICPRICPR-2012-ZhangH #feature model #recognition #using
Face recognition using semi-supervised spectral feature selection (ZZ, ERH), pp. 1294–1297.
ICPRICPR-2012-ZhangH12a #feature model #recognition
Unsupervised spectral feature selection for face recognition (ZZ, ERH), pp. 1787–1790.
KDDKDD-2012-CorreaL #clustering #graph #using
Locally-scaled spectral clustering using empty region graphs (CDC, PL), pp. 1330–1338.
KDDKDD-2012-WauthierJJ #clustering #nondeterminism #reduction
Active spectral clustering via iterative uncertainty reduction (FLW, NJ, MIJ), pp. 1339–1347.
KDIRKDIR-2012-VolkovichA #clustering
Model Selection and Stability in Spectral Clustering (ZV, RA), pp. 25–34.
HPDCHPDC-2012-HefeedaGA #approximate #clustering #dataset #distributed #scalability
Distributed approximate spectral clustering for large-scale datasets (MH, FG, WAA), pp. 223–234.
ICSTICST-2012-MalikK #analysis #graph #using
Dynamic Shape Analysis Using Spectral Graph Properties (MZM, SK), pp. 211–220.
HCIHCI-MIIE-2011-MinKH #recognition #using
Spectral Subtraction Based Emotion Recognition Using EEG (JHM, HOK, KSH), pp. 569–576.
CIKMCIKM-2011-KimKFL #analysis
Spectral analysis of a blogosphere (SWK, KNK, CF, JHL), pp. 2145–2148.
ICMLICML-2011-DasK #algorithm #approximate #set #taxonomy
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection (AD, DK), pp. 1057–1064.
ICMLICML-2011-KumarD #approach #clustering #multi
A Co-training Approach for Multi-view Spectral Clustering (AK, HDI), pp. 393–400.
ICMLICML-2011-ParikhSX #algorithm #modelling #visual notation
A Spectral Algorithm for Latent Tree Graphical Models (APP, LS, EPX), pp. 1065–1072.
KDIRKDIR-2011-AzamV #clustering #comparative #evaluation #metric #proximity
A Comparative Evaluation of Proximity Measures for Spectral Clustering (NFA, HLV), pp. 30–41.
STOCSTOC-2010-Kannan #matrix
Spectral methods for matrices and tensors (RK), pp. 1–12.
STOCSTOC-2010-RaghavendraST #approximate #graph #parametricity
Approximations for the isoperimetric and spectral profile of graphs and related parameters (PR, DS, PT), pp. 631–640.
CIKMCIKM-2010-FangSS #clustering #learning #multi
Multilevel manifold learning with application to spectral clustering (HrF, SS, YS), pp. 419–428.
CIKMCIKM-2010-KunegisFB #evolution #network
Network growth and the spectral evolution model (JK, DF, CB), pp. 739–748.
ICMLICML-2010-BshoutyL #clustering #linear #using
Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering (NHB, PML), pp. 135–142.
ICMLICML-2010-NiuDJ #clustering #multi
Multiple Non-Redundant Spectral Clustering Views (DN, JGD, MIJ), pp. 831–838.
ICPRICPR-2010-BatesLM #representation
Scale-Space Spectral Representation of Shape (JB, XL, WM), pp. 2648–2651.
ICPRICPR-2010-BourlaiKRCH #verification
Cross-Spectral Face Verification in the Short Wave Infrared (SWIR) Band (TB, NDK, AR, BC, LH), pp. 1343–1347.
ICPRICPR-2010-BozkurtEEE #recognition #speech
Use of Line Spectral Frequencies for Emotion Recognition from Speech (EB, EE, ÇEE, ATE), pp. 3708–3711.
ICPRICPR-2010-IbrahimTH #image #invariant #representation
Spectral Invariant Representation for Spectral Reflectance Image (AI, ST, TH), pp. 2776–2779.
ICPRICPR-2010-LettnerS #robust
Combining Spectral and Spatial Features for Robust Foreground-Background Separation (ML, RS), pp. 1969–1972.
ICPRICPR-2010-ManciniFZ #detection #multi
Road Change Detection from Multi-Spectral Aerial Data (AM, EF, PZ), pp. 448–451.
ICPRICPR-2010-SfikasHN #analysis #clustering #multi #using
Multiple Atlas Inference and Population Analysis Using Spectral Clustering (GS, CH, CN), pp. 2500–2503.
ICPRICPR-2010-SmeetsFHVS #3d #approach #composition #invariant #modelling #recognition #using
Fusion of an Isometric Deformation Modeling Approach Using Spectral Decomposition and a Region-Based Approach Using ICP for Expression-Invariant 3D Face Recognition (DS, TF, JH, DV, PS), pp. 1172–1175.
ICPRICPR-2010-TekeT #image #multi #strict #using
Multi-spectral Satellite Image Registration Using Scale-Restricted SURF (MT, AT), pp. 2310–2313.
ICPRICPR-2010-XuV
Binary Representations of Fingerprint Spectral Minutiae Features (HX, RNJV), pp. 1212–1216.
KDDKDD-2010-WangD #clustering #flexibility
Flexible constrained spectral clustering (XW, ID), pp. 563–572.
DACDAC-2009-CochranR
Spectral techniques for high-resolution thermal characterization with limited sensor data (RC, SR), pp. 478–483.
ICDARICDAR-2009-LettnerS #documentation #image #multi #segmentation
Spatial and Spectral Based Segmentation of Text in Multispectral Images of Ancient Documents (ML, RS), pp. 813–817.
ICMLICML-2009-BuhlerH #clustering #graph
Spectral clustering based on the graph p-Laplacian (TB, MH), pp. 81–88.
ICMLICML-2009-KumarMT #approximate #composition #on the
On sampling-based approximate spectral decomposition (SK, MM, AT), pp. 553–560.
ICMLICML-2009-KunegisL #graph transformation #learning #predict
Learning spectral graph transformations for link prediction (JK, AL), pp. 561–568.
KDDKDD-2009-YanHJ #approximate #clustering #performance
Fast approximate spectral clustering (DY, LH, MIJ), pp. 907–916.
MLDMMLDM-2009-SakaiI #clustering #performance #random
Fast Spectral Clustering with Random Projection and Sampling (TS, AI), pp. 372–384.
CASECASE-2008-SunEHMMMBLM #analysis #automation #biology #integration #multi #user interface
Integration of user interface, device control, data acquisition and analysis for automated multi-spectral imaging of single biological cells (CSS, JRE, MH, TWM, SKM, SM, LWB, MEL, DRM), pp. 1013–1018.
DATEDATE-2008-TcheghoMS
Optimal High-Resolution Spectral Analyzer (AT, HM, SS), pp. 62–67.
ICMLICML-2008-ColemanSW #clustering #consistency
Spectral clustering with inconsistent advice (TC, JS, AW), pp. 152–159.
ICPRICPR-2008-AndriyashinPJ #dependence
Illuminant dependence of PCA, NMF and NTF in spectral color imaging (AA, JP, TJ), pp. 1–4.
ICPRICPR-2008-LezorayTE #clustering #graph
Impulse noise removal by spectral clustering and regularization on graphs (OL, VTT, AE), pp. 1–4.
ICPRICPR-2008-LiuDJM #3d #kernel #robust
Kernel functions for robust 3D surface registration with spectral embeddings (XL, AD, MJ, WM), pp. 1–4.
ICPRICPR-2008-LiZWH #analysis #clustering #multi
Multiclass spectral clustering based on discriminant analysis (XL, ZZ, YW, WH), pp. 1–4.
ICPRICPR-2008-XiaoWH #graph #invariant #recognition #using
Object recognition using graph spectral invariants (XB, RCW, ERH), pp. 1–4.
KDDKDD-2008-LingDXYY #learning
Spectral domain-transfer learning (XL, WD, GRX, QY, YY), pp. 488–496.
KDDKDD-2008-SunJY #classification #learning #multi
Hypergraph spectral learning for multi-label classification (LS, SJ, JY), pp. 668–676.
SIGIRSIGIR-2008-LiuLLJ #clustering #geometry #query #ranking
Spectral geometry for simultaneously clustering and ranking query search results (YL, WL, YL, LJ), pp. 539–546.
DATEDATE-2007-ZhuZCXZ #grid #probability #process
A sparse grid based spectral stochastic collocation method for variations-aware capacitance extraction of interconnects under nanometer process technology (HZ, XZ, WC, JX, DZ), pp. 1514–1519.
ICDARICDAR-2007-PapavassiliouSKC #parametricity #verification
A Parametric Spectral-Based Method for Verification of Text in Videos (VP, TS, VK, GC), pp. 879–883.
CIKMCIKM-2007-CaiHZH #locality
Regularized locality preserving indexing via spectral regression (DC, XH, WVZ, JH), pp. 741–750.
ECIRECIR-2007-LiuYZQM #clustering #optimisation #performance #scalability
Fast Large-Scale Spectral Clustering by Sequential Shrinkage Optimization (TYL, HYY, XZ, TQ, WYM), pp. 319–330.
ICMLICML-2007-LiYW #distance #framework #learning #metric #reduction
A transductive framework of distance metric learning by spectral dimensionality reduction (FL, JY, JW), pp. 513–520.
ICMLICML-2007-TomiokaA #matrix
Classifying matrices with a spectral regularization (RT, KA), pp. 895–902.
ICMLICML-2007-ZhaoL #feature model #learning
Spectral feature selection for supervised and unsupervised learning (ZZ, HL), pp. 1151–1157.
ICMLICML-2007-ZhouB #clustering #learning #multi
Spectral clustering and transductive learning with multiple views (DZ, CJCB), pp. 1159–1166.
KDDKDD-2007-ChiSZHT #clustering
Evolutionary spectral clustering by incorporating temporal smoothness (YC, XS, DZ, KH, BLT), pp. 153–162.
KDDKDD-2007-ShigaTM #approach #clustering #composition #network
A spectral clustering approach to optimally combining numericalvectors with a modular network (MS, IT, HM), pp. 647–656.
ICALPICALP-v1-2006-Coja-Oghlan #adaptation #clustering #graph #heuristic #random
An Adaptive Spectral Heuristic for Partitioning Random Graphs (ACO), pp. 691–702.
ICALPICALP-v1-2006-Coja-OghlanL #graph #random
The Spectral Gap of Random Graphs with Given Expected Degrees (ACO, AL), pp. 15–26.
ICMLICML-2006-LongZWY #clustering #multi #relational
Spectral clustering for multi-type relational data (BL, Z(Z, XW, PSY), pp. 585–592.
ICMLICML-2006-MoghaddamWA #bound
Generalized spectral bounds for sparse LDA (BM, YW, SA), pp. 641–648.
ICMLICML-2006-XiaoSB #reduction
A duality view of spectral methods for dimensionality reduction (LX, JS, SPB), pp. 1041–1048.
ICMLICML-2006-ZhangK #kernel #matrix #performance
Block-quantized kernel matrix for fast spectral embedding (KZ, JTK), pp. 1097–1104.
ICPRICPR-v1-2006-FuTC #orthogonal #using
Specular Free Spectral Imaging Using Orthogonal Subspace Projection (ZF, RTT, TC), pp. 812–815.
ICPRICPR-v2-2006-ScarpaH #clustering #independence #segmentation
Unsupervised Texture Segmentation by Spectral-Spatial-Independent Clustering (GS, MH), pp. 151–154.
ICPRICPR-v4-2006-WhiteW #graph
Mixing spectral representations of graphs (DHW, RCW), pp. 140–144.
SACSAC-2006-GuoW #data mining #mining #on the #privacy
On the use of spectral filtering for privacy preserving data mining (SG, XW), pp. 622–626.
STOCSTOC-2005-Vu #matrix #random
Spectral norm of random matrices (VHV), pp. 423–430.
ICMLICML-2005-ShaS #analysis #reduction
Analysis and extension of spectral methods for nonlinear dimensionality reduction (FS, LKS), pp. 784–791.
SIGIRSIGIR-2005-BastM #retrieval #why
Why spectral retrieval works (HB, DM), pp. 11–18.
STOCSTOC-2004-Kelner #bound #clustering #graph
Spectral partitioning, eigenvalue bounds, and circle packings for graphs of bounded genus (JAK), pp. 455–464.
STOCSTOC-2004-KempeM #algorithm #analysis #distributed
A decentralized algorithm for spectral analysis (DK, FM), pp. 561–568.
ICMLICML-2004-DingH #clustering
Linearized cluster assignment via spectral ordering (CHQD, XH).
ICPRICPR-v1-2004-ChabrierELRM #evaluation #image #multi #segmentation
Unsupervised Evaluation of Image Segmentation Application to Multi-spectral Images (SC, BE, HL, CR, PM), pp. 576–579.
ICPRICPR-v1-2004-KalenovaTB #image
Spectral Image Distortion Map (DK, PJT, VB), pp. 668–671.
ICPRICPR-v2-2004-CostaGB
Spectral Characterization of Orientation Data along Curvilinear Structures (JPDC, CG, PB), pp. 517–520.
ICPRICPR-v2-2004-DroriFY
Spectral Sound Gap Filling (ID, AF, YY), pp. 871–874.
ICPRICPR-v2-2004-LindgrenH #component #image #independence #learning #representation
Learning High-level Independent Components of Images through a Spectral Representation (JTL, AH), pp. 72–75.
ICPRICPR-v2-2004-PranckevicieneBS #classification #identification
Consensus-Based Identification of Spectral Signatures for Classification of High-Dimensional Biomedical Spectra (EP, RB, RLS), pp. 319–322.
ICPRICPR-v2-2004-Srinivasan #approximate #segmentation
Small-world Approximations in Spectral Segmentation (SHS), pp. 36–39.
ICPRICPR-v2-2004-WilsonH #distance #graph
Levenshtein Distance for Graph Spectral Features (RCW, ERH), pp. 489–492.
ICPRICPR-v3-2004-BaiYH #graph #using
Graph Matching using Spectral Embedding and Alignment (XB, HY, ERH), pp. 398–401.
ICPRICPR-v3-2004-LuoWH #graph
Graph Manifolds from Spectral Polynomials (BL, RCW, ERH), pp. 402–405.
ICPRICPR-v4-2004-PaclikVD #algorithm #feature model #multi
Multi-Class Extensions of the GLDB Feature Extraction Algorithm for Spectral Data (PP, SV, RPWD), pp. 629–632.
ICPRICPR-v4-2004-ParkJZK #clustering #graph
Support Vector Clustering Combined with Spectral Graph Partitioning (JHP, XJ, HZ, RK), pp. 581–584.
KDDKDD-2004-DhillonGK #clustering #kernel #normalisation
Kernel k-means: spectral clustering and normalized cuts (ISD, YG, BK), pp. 551–556.
DACDAC-2003-VasudevanR #using
Computation of noise spectral density in switched capacitor circuits using the mixed-frequency-time technique (VV, MR), pp. 538–541.
DATEDATE-2003-LupeaPJ #analysis #named
RF-BIST: Loopback Spectral Signature Analysis (DL, UP, HJJ), pp. 10478–10483.
SIGMODSIGMOD-2003-CohenM
Spectral Bloom Filters (SC, YM), pp. 241–252.
ICMLICML-2003-Joachims #clustering #graph #learning
Transductive Learning via Spectral Graph Partitioning (TJ), pp. 290–297.
WCREWCRE-2002-ShokoufandehMM #clustering
Applying Spectral Methods to Software Clustering (AS, SM, MM), pp. 3–10.
ICPRICPR-v1-2002-CarcassoniRH #analysis #recognition
Texture Recognition through Modal Analysis of Spectral Peak Patterns (MC, ER, ERH), pp. 243–246.
ICPRICPR-v1-2002-CarvalhoSDR #constraints #estimation #linear
Bayes Information Criterion for Tikhonov Regularization with Linear Constraints: Application to Spectral Data Estimation (PDC, AS, AD, BR), pp. 696–700.
ICPRICPR-v1-2002-LiuS #classification #recognition #representation
A Spectral Representation for Appearance-Based Classification and Recognition (XL, AS), pp. 37–40.
ICPRICPR-v3-2002-LuoWH02a #approach #graph #learning
Graph Spectral Approach for Learning View Structure (BL, RCW, ERH), pp. 785–788.
ICPRICPR-v3-2002-Robles-KellyH #approach
A Graph-Spectral Approach to Surface Segmentatio (ARK, ERH), pp. 509–508.
ICPRICPR-v4-2002-Robles-KellyH02a #approach
A Graph-Spectral Approach to Correspondence Matching (ARK, ERH), pp. 176–179.
SIGIRSIGIR-2002-Shalev-ShwartzDFS #modelling #query #robust
Robust temporal and spectral modeling for query By melody (SSS, SD, NF, YS), pp. 331–338.
DATEDATE-2001-GianiSHA #performance
Efficient spectral techniques for sequential ATPG (AG, SS, MSH, VDA), pp. 204–208.
DATEDATE-2001-ThorntonD #diagrams #graph transformation #using
Spectral decision diagrams using graph transformations (MAT, RD), pp. 713–719.
STOCSTOC-2001-AzarFKMS #analysis
Spectral analysis of data (YA, AF, ARK, FM, JS), pp. 619–626.
KDDKDD-2001-Dhillon #clustering #documentation #graph #using #word
Co-clustering documents and words using bipartite spectral graph partitioning (ISD), pp. 269–274.
KDDKDD-2001-DingHZ #component #graph #web
A spectral method to separate disconnected and nearly-disconnected web graph components (CHQD, XH, HZ), pp. 275–280.
ICPRICPR-v1-2000-RibeiroH
Texture Plane Orientation from Spectral Accumulation (ER, ERH), pp. 1802–1806.
ICPRICPR-v1-2000-TominagaO #multi #recognition
Object Recognition by Multi-Spectral Imaging with a Liquid Crystal Filter (ST, RO), pp. 1708–1711.
ICPRICPR-v3-2000-CamilleriP #bound #refinement
Spectral Unmixing of Mixed Pixels for Texture Boundary Refinement (KPC, MP), pp. 7096–7099.
ICPRICPR-v3-2000-ManabeKC #metric
Simultaneous Measurement of Spectral Distribution and Shape (YM, SK, KC), pp. 3811–3814.
ICPRICPR-v3-2000-RibeiroH00a #adaptation
A Scale Adaptive Method for Focusing Spectral Peaks (ER, ERH), pp. 3123–3126.
ICPRICPR-v3-2000-Tajima #evaluation
Consideration and Experiments on Object Spectral Reflectance for Color Sensor Evaluation/Calibration (JT), pp. 3592–3595.
ICPRICPR-v4-2000-KhorsheedC #multi #recognition #using #word
Multi-Font Arabic Word Recognition Using Spectral Features (MSK, WFC), pp. 4543–4546.
ICPRICPR-1998-ManabeZI #estimation #geometry #image
Estimation of illuminant spectral distribution with geometrical information from spectral image (YM, XZ, SI), pp. 1464–1466.
ICPRICPR-1998-NakaiMI #analysis #simulation
Simulation and analysis of spectral distributions of human skin (HN, YM, SI), pp. 1065–1067.
STOCSTOC-1997-LaffertyR
Spectral Techniques for Expander Codes (JDL, DNR), pp. 160–167.
DACDAC-1996-HansenS #diagrams #synthesis #using
Synthesis by Spectral Translation Using Boolean Decision Diagrams (JPH, MS), pp. 248–253.
DACDAC-1996-LiLLC #approach #clustering #linear
New Spectral Linear Placement and Clustering Approach (JL, JL, LTL, CKC), pp. 88–93.
ICPRICPR-1996-Ben-ArieWR #invariant #recognition
Iconic recognition with affine-invariant spectral signatures (JBA, ZW, KRR), pp. 672–676.
ICPRICPR-1996-LabonteDC #representation #sequence
A compact representation for stereoscopic sequences with NTSC spectral compatibility (FL, CTLD, PC), pp. 646–650.
ICPRICPR-1996-ManabeI #image #recognition #using
Recognition of material types using spectral image (YM, SI), pp. 840–843.
ICPRICPR-1996-SenguptaB #clustering #using
Using spectral features for modelbase partitioning (KS, KLB), pp. 65–69.
ICPRICPR-1996-WindeattT #feature model
Analytical feature extraction and spectral summation (TW, RT), pp. 315–319.
DACDAC-1995-AlpertY #clustering
Spectral Partitioning: The More Eigenvectors, The Better (CJA, SZY), pp. 195–200.
KDDKDD-1995-BuntineP #how
Intelligent Instruments: Discovering How to Turn Spectral Data into Information (WLB, TP), pp. 33–38.
STOCSTOC-1994-AlonK #graph #random
A spectral technique for coloring random 3-colorable graphs (preliminary version) (NA, NK), pp. 346–355.
DACDAC-1993-ChanSZ93a #clustering
Spectral K-Way Ratio-Cut Partitioning and Clustering (PKC, MDFS, JYZ), pp. 749–754.
DACDAC-1993-ClarkeMZFY #scalability
Spectral Transforms for Large Boolean Functions with Applications to Technology Mapping (EMC, KLM, XZ, MF, JY), pp. 54–60.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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