BibSLEIGH
BibSLEIGH corpus
BibSLEIGH tags
BibSLEIGH bundles
BibSLEIGH people
CC-BY
Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
email twitter
Used together with:
semi (184)
learn (152)
use (56)
classif (45)
base (41)

Stem supervis$ (all stems)

363 papers:

ICPCICPC-2015-ThungLL #categorisation #fault
Active semi-supervised defect categorization (FT, XBDL, DL), pp. 60–70.
FMFM-2015-Lecomte #modelling #verification
Formal Virtual Modelling and Data Verification for Supervision Systems (TL), pp. 597–600.
ICEISICEIS-v1-2015-PecliGPMFTTDFCG #learning #predict #problem #reduction
Dimensionality Reduction for Supervised Learning in Link Prediction Problems (AP, BG, CCP, CM, FF, FT, JT, MVD, SF, MCC, RRG), pp. 295–302.
ICMLICML-2015-HockingRB #detection #learning #named #segmentation
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data (TH, GR, GB), pp. 324–332.
KDDKDD-2015-LanH #complexity #learning #multi
Reducing the Unlabeled Sample Complexity of Semi-Supervised Multi-View Learning (CL, JH), pp. 627–634.
KDDKDD-2015-Yi0YLW #algorithm #clustering #constraints #performance
An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints (JY, LZ, TY, WL, JW), pp. 1405–1414.
KDDKDD-2015-ZhengYX #composition #linear #modelling #topic #using
Linear Time Samplers for Supervised Topic Models using Compositional Proposals (XZ, YY, EPX), pp. 1523–1532.
MLDMMLDM-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.
MLDMMLDM-2015-TreechalongRW #clustering #using
Semi-Supervised Stream Clustering Using Labeled Data Points (KT, TR, KW), pp. 281–295.
SIGIRSIGIR-2015-PanYLNM #scalability #semantics #visual notation
Semi-supervised Hashing with Semantic Confidence for Large Scale Visual Search (YP, TY, HL, CWN, TM), pp. 53–62.
SACSAC-2015-BerardiEF0 #classification #mobile #multi
Multi-store metadata-based supervised mobile app classification (GB, AE, TF, FS), pp. 585–588.
SACSAC-2015-FauconnierKR #approach #machine learning #recognition #taxonomy
A supervised machine learning approach for taxonomic relation recognition through non-linear enumerative structures (JPF, MK, BR), pp. 423–425.
SACSAC-2015-RegoMP #approach #detection #folksonomy #learning
A supervised learning approach to detect subsumption relations between tags in folksonomies (ASdCR, LBM, CESP), pp. 409–415.
SACSAC-2015-SilvaBAR #clustering #multi #prototype #using
Semi-supervised clustering using multi-assistant-prototypes to represent each cluster (WJS, MCNB, SdA, HLR), pp. 831–836.
CASECASE-2014-MarkovskiH #framework #modelling #reliability
A synthesis-centric model-based systems engineering framework for reliable supervision of systems with general distributions (JM, HH), pp. 436–442.
VLDBVLDB-2014-0001PK
Supervised Meta-blocking (GP, GP, GK), pp. 1929–1940.
HCIDHM-2014-FacoettiGV
An Environment for Domestic Supervised Amblyopia Treatment (GF, AG, AV), pp. 340–350.
CIKMCIKM-2014-LengCWZL #constraints
Supervised Hashing with Soft Constraints (CL, JC, JW, XZ, HL), pp. 1851–1854.
CIKMCIKM-2014-Wen0R #precise
Enabling Precision/Recall Preferences for Semi-supervised SVM Training (ZW, RZ, KR), pp. 421–430.
CIKMCIKM-2014-ZhukovskiyGS #rank
Supervised Nested PageRank (MZ, GG, PS), pp. 1059–1068.
ICMLICML-c1-2014-SolomonRGB #learning
Wasserstein Propagation for Semi-Supervised Learning (JS, RMR, LJG, AB), pp. 306–314.
ICMLICML-c1-2014-ZhouT #generative #network #predict #probability
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction (JZ, OGT), pp. 745–753.
ICMLICML-c2-2014-ClemenconR #ranking
Anomaly Ranking as Supervised Bipartite Ranking (SC, SR), pp. 343–351.
ICMLICML-c2-2014-FangCL #graph #learning
Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically (YF, KCCC, HWL), pp. 406–414.
ICMLICML-c2-2014-LiZ #higher-order #learning #problem
High Order Regularization for Semi-Supervised Learning of Structured Output Problems (YL, RSZ), pp. 1368–1376.
ICMLICML-c2-2014-SongGJMHD #learning #locality #on the
On learning to localize objects with minimal supervision (HOS, RBG, SJ, JM, ZH, TD), pp. 1611–1619.
ICMLICML-c2-2014-XuTXR #reduction
Large-margin Weakly Supervised Dimensionality Reduction (CX, DT, CX, YR), pp. 865–873.
ICPRICPR-2014-BertonL #graph #learning
Graph Construction Based on Labeled Instances for Semi-supervised Learning (LB, AdAL), pp. 2477–2482.
ICPRICPR-2014-BouillonA #classification #evolution #gesture #learning #online
Supervision Strategies for the Online Learning of an Evolving Classifier for Gesture Commands (MB, ÉA), pp. 2029–2034.
ICPRICPR-2014-ChengZHT #learning #recognition
Semi-supervised Learning for RGB-D Object Recognition (YC, XZ, KH, TT), pp. 2377–2382.
ICPRICPR-2014-DuDA #modelling #topic #using
Signature Matching Using Supervised Topic Models (XD, DSD, WAA), pp. 327–332.
ICPRICPR-2014-IosifidisTP #classification #network
Semi-supervised Classification of Human Actions Based on Neural Networks (AI, AT, IP), pp. 1336–1341.
ICPRICPR-2014-KrytheL #analysis #linear
Implicitly Constrained Semi-supervised Linear Discriminant Analysis (JHK, ML), pp. 3762–3767.
ICPRICPR-2014-KunwarPB #network #online #recognition
Semi-supervised Online Bayesian Network Learner for Handwritten Characters Recognition (RK, UP, MB), pp. 3104–3109.
ICPRICPR-2014-LiuYHTH #learning #recognition #visual notation
Semi-supervised Learning for Cross-Device Visual Location Recognition (PL, PY, KH, TT, HWH), pp. 2873–2878.
ICPRICPR-2014-Ozay #image #multi #segmentation
Semi-supervised Segmentation Fusion of Multi-spectral and Aerial Images (MO), pp. 3839–3844.
ICPRICPR-2014-PhamKC #graph #image #learning
Semi-supervised Learning on Bi-relational Graph for Image Annotation (HDP, KHK, SC), pp. 2465–2470.
ICPRICPR-2014-QianZX #classification #multi #social
Boosted Multi-modal Supervised Latent Dirichlet Allocation for Social Event Classification (SQ, TZ, CX), pp. 1999–2004.
ICPRICPR-2014-RehnS #locality #visual notation
Nonlinear Supervised Locality Preserving Projections for Visual Pattern Discrimination (EMR, HS), pp. 1568–1573.
ICPRICPR-2014-RozzaMP #graph #kernel #learning #novel
A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning (AR, MM, AP), pp. 3786–3791.
ICPRICPR-2014-SaitoAFRSGC #learning #using
Active Semi-supervised Learning Using Optimum-Path Forest (PTMS, WPA, AXF, PJdR, CTNS, JFG, MHdC), pp. 3798–3803.
ICPRICPR-2014-WangSWB #consistency
Label Consistent Fisher Vectors for Supervised Feature Aggregation (QW, XS, MW, KLB), pp. 3588–3593.
ICPRICPR-2014-WangT #classification
Label-Denoising Auto-encoder for Classification with Inaccurate Supervision Information (DW, XT), pp. 3648–3653.
ICPRICPR-2014-YangN #integration #learning #multi
Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration (YY, SN), pp. 4062–4067.
ICPRICPR-2014-ZhangQWL #classification #learning #online
Object Classification in Traffic Scene Surveillance Based on Online Semi-supervised Active Learning (ZZ, JQ, YW, ML), pp. 3086–3091.
KDDKDD-2014-GaddeAO #graph #learning #using
Active semi-supervised learning using sampling theory for graph signals (AG, AA, AO), pp. 492–501.
KDDKDD-2014-GunnemannFRS #clustering #multi #named
SMVC: semi-supervised multi-view clustering in subspace projections (SG, IF, MR, TS), pp. 253–262.
KDDKDD-2014-TayebiEGB #embedded #learning #predict #using
Spatially embedded co-offence prediction using supervised learning (MAT, ME, UG, PLB), pp. 1789–1798.
KDDKDD-2014-WangNH #adaptation #induction #learning #scalability
Large-scale adaptive semi-supervised learning via unified inductive and transductive model (DW, FN, HH), pp. 482–491.
KDDKDD-2014-ZhangTMF #learning #network
Supervised deep learning with auxiliary networks (JZ, GT, YM, WF), pp. 353–361.
MLDMMLDM-2014-YuST #detection #modelling #realtime
Semi-supervised Time Series Modeling for Real-Time Flux Domain Detection on Passive DNS Traffic (BY, LS, MT), pp. 258–271.
SIGIRSIGIR-2014-HaiCCLC #bibliography #sentiment
Coarse-to-fine review selection via supervised joint aspect and sentiment model (ZH, GC, KC, WL, PC), pp. 617–626.
SIGIRSIGIR-2014-HingmireC #approach #classification #topic
Topic labeled text classification: a weakly supervised approach (SH, SC), pp. 385–394.
SIGIRSIGIR-2014-ZhangTZX #algorithm #recommendation
Addressing cold start in recommender systems: a semi-supervised co-training algorithm (MZ, JT, XZ, XX), pp. 73–82.
SIGIRSIGIR-2014-ZhangZLG #modelling
Supervised hashing with latent factor models (PZ, WZ, WJL, MG), pp. 173–182.
SACSAC-2014-Zheng #matrix #using
Semi-supervised context-aware matrix factorization: using contexts in a way of “latent” factors (YZ), pp. 292–293.
SACSAC-2014-ZimmermannNS #adaptation #classification
Adaptive semi supervised opinion classifier with forgetting mechanism (MZ, EN, MS), pp. 805–812.
DocEngDocEng-2013-VilaresAG #classification #twitter
Supervised polarity classification of Spanish tweets based on linguistic knowledge (DV, MAA, CGR), pp. 169–172.
DRRDRR-2013-WalkerRS #fault #modelling #topic
Evaluating supervised topic models in the presence of OCR errors (DDW, EKR, KDS).
ICDARICDAR-2013-BougueliaBB #approach #classification #documentation #learning
A Stream-Based Semi-supervised Active Learning Approach for Document Classification (MRB, YB, AB), pp. 611–615.
ICDARICDAR-2013-RoySSJ #documentation #n-gram #segmentation #using
Character N-Gram Spotting on Handwritten Documents Using Weakly-Supervised Segmentation (UR, NS, KPS, CVJ), pp. 577–581.
ICDARICDAR-2013-ZhongCC #empirical #evaluation #recognition #reduction
An Empirical Evaluation of Supervised Dimensionality Reduction for Recognition (GZ, YC, MC), pp. 1315–1319.
CSMRCSMR-2013-XiaLWYLS #algorithm #case study #comparative #debugging #learning #predict
A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction (XX, DL, XW, XY, SL, JS), pp. 331–334.
HCIHCI-III-2013-HeLWG #image #segmentation #using
Semi-supervised Remote Sensing Image Segmentation Using Dynamic Region Merging (NH, KL, YW, YG), pp. 153–162.
CIKMCIKM-2013-BaoCD #classification #topic
A partially supervised cross-collection topic model for cross-domain text classification (YB, NC, AD), pp. 239–248.
CIKMCIKM-2013-FangHZ #analysis #bibliography #sentiment
Exploring weakly supervised latent sentiment explanations for aspect-level review analysis (LF, MH, XZ), pp. 1057–1066.
CIKMCIKM-2013-VolkovsZ #framework
CRF framework for supervised preference aggregation (MV, RSZ), pp. 89–98.
CIKMCIKM-2013-XuXWW #automation #feedback #image #ranking
A heterogenous automatic feedback semi-supervised method for image reranking (XCX, XSX, YW, XW), pp. 999–1008.
ICMLICML-c1-2013-BalcanBEL #learning #performance
Efficient Semi-supervised and Active Learning of Disjunctions (NB, CB, SE, YL), pp. 633–641.
ICMLICML-c3-2013-NiuJDHS #approach #learning #novel
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning (GN, WJ, BD, HH, MS), pp. 10–18.
ICMLICML-c3-2013-OgawaITS
Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines (KO, MI, IT, MS), pp. 897–905.
ICMLICML-c3-2013-TarlowSCSZ #learning #probability
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning (DT, KS, LC, IS, RSZ), pp. 199–207.
ICMLICML-c3-2013-YiZJQJ #clustering #matrix #similarity
Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion (JY, LZ, RJ, QQ, AKJ), pp. 1400–1408.
KDDKDD-2013-ChenHKB #learning #named
DTW-D: time series semi-supervised learning from a single example (YC, BH, EJK, GEAPAB), pp. 383–391.
KDDKDD-2013-HuaCZLR #detection #named #twitter
STED: semi-supervised targeted-interest event detectionin in twitter (TH, FC, LZ, CTL, NR), pp. 1466–1469.
KDDKDD-2013-RaederPDSP #clustering #reduction #scalability #using
Scalable supervised dimensionality reduction using clustering (TR, CP, BD, OS, FJP), pp. 1213–1221.
KDIRKDIR-KMIS-2013-VensVB #clustering
Semi-supervised Clustering with Example Clusters (CV, BV, HB), pp. 45–51.
MLDMMLDM-2013-MinhAN #algorithm #feature model
DCA Based Algorithms for Feature Selection in Semi-supervised Support Vector Machines (LHM, LTHA, MCN), pp. 528–542.
SIGIRSIGIR-2013-BonnefoyBB #detection #documentation
A weakly-supervised detection of entity central documents in a stream (LB, VB, PB), pp. 769–772.
SACSAC-2013-AkritidisB #algorithm #classification #machine learning #research
A supervised machine learning classification algorithm for research articles (LA, PB), pp. 115–120.
SACSAC-2013-HassanzadehN #algorithm #detection #graph
A semi-supervised graph-based algorithm for detecting outliers in online-social-networks (RH, RN), pp. 577–582.
ASEASE-2012-LuCC #fault #learning #predict #reduction #using
Software defect prediction using semi-supervised learning with dimension reduction (HL, BC, MC), pp. 314–317.
CASECASE-2012-Markovski #framework
A process-theoretic state-based framework for live supervision (JM), pp. 680–685.
DocEngDocEng-2012-HuMBL #clustering #documentation #personalisation
Personalized document clustering with dual supervision (YH, EEM, JB, SL), pp. 161–170.
ICSMEICSM-2012-HallWM #composition
Supervised software modularisation (MH, NW, PM), pp. 472–481.
IFMIFM-2012-MarkovskiBB #component #requirements
Partially-Supervised Plants: Embedding Control Requirements in Plant Components (JM, DAvB, JCMB), pp. 253–267.
CIKMCIKM-2012-BlessingS #complexity
Crosslingual distant supervision for extracting relations of different complexity (AB, HS), pp. 1123–1132.
CIKMCIKM-2012-JuLSZHL #classification #documentation #sentiment #word
Dual word and document seed selection for semi-supervised sentiment classification (SJ, SL, YS, GZ, YH, XL), pp. 2295–2298.
CIKMCIKM-2012-MaoHYL #integration #modelling #topic
Hierarchical topic integration through semi-supervised hierarchical topic modeling (XM, JH, HY, XL), pp. 1612–1616.
CIKMCIKM-2012-QuanzH #generative #learning #multi #named
CoNet: feature generation for multi-view semi-supervised learning with partially observed views (BQ, JH), pp. 1273–1282.
CIKMCIKM-2012-WangWYHDC #framework #learning #modelling #novel
A novel local patch framework for fixing supervised learning models (YW, BW, JY, YH, ZHD, ZC), pp. 1233–1242.
ICMLICML-2012-JiYLJH #algorithm #bound #fault #learning
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound (MJ, TY, BL, RJ, JH), p. 110.
ICMLICML-2012-JoulinB #classification
A convex relaxation for weakly supervised classifiers (AJ, FRB), p. 171.
ICMLICML-2012-LinXWZ #learning
Total Variation and Euler’s Elastica for Supervised Learning (TL, HX, LW, HZ), p. 82.
ICMLICML-2012-McDowellA #classification #hybrid
Semi-Supervised Collective Classification via Hybrid Label Regularization (LM, DWA), p. 162.
ICMLICML-2012-NiuDYS #learning #metric
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization (GN, BD, MY, MS), p. 136.
ICMLICML-2012-PlessisS #learning
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching (MCdP, MS), p. 159.
ICPRICPR-2012-BaiXP #classification #image
Classification oriented semi-supervised band selection for hyperspectral images (JB, SX, CP), pp. 1888–1891.
ICPRICPR-2012-BespalovQBS #classification #image #scalability #using
Large-scale image classification using supervised spatial encoder (DB, YQ, BB, AS), pp. 581–584.
ICPRICPR-2012-CermanH #learning #problem
Tracking with context as a semi-supervised learning and labeling problem (LC, VH), pp. 2124–2127.
ICPRICPR-2012-CourtyAL #approach #classification #image
A classwise supervised ordering approach for morphology based hyperspectral image classification (NC, EA, SL), pp. 1997–2000.
ICPRICPR-2012-DinhDL #case study #difference #representation
A study on semi-supervised dissimilarity representation (VCD, RPWD, ML), pp. 2861–2864.
ICPRICPR-2012-HidoK #graph #learning #similarity
Hash-based structural similarity for semi-supervised Learning on attribute graphs (SH, HK), pp. 3009–3012.
ICPRICPR-2012-LiL12a #image #invariant #segmentation
Scale-invariant sampling for supervised image segmentation (YL, ML), pp. 1399–1402.
ICPRICPR-2012-LiWBL #adaptation #algorithm #fault
Semi-supervised adaptive parzen Gentleboost algorithm for fault diagnosis (CL, ZW, SB, ZL), pp. 2290–2293.
ICPRICPR-2012-PourdamghaniRZ #estimation #graph #learning #metric
Metric learning for graph based semi-supervised human pose estimation (NP, HRR, MZ), pp. 3386–3389.
ICPRICPR-2012-RoyH #classification #component #detection #documentation #image #using
Text detection on camera acquired document images using supervised classification of connected components in wavelet domain (UR, GH), pp. 270–273.
ICPRICPR-2012-WongLTYCCBW #approach #automation #graph #locality
Automatic localization of the macula in a supervised graph-based approach with contextual superpixel features (DWKW, JL, NMT, FY, XC, CMGC, MB, TYW), pp. 2063–2066.
ICPRICPR-2012-ZhangH #feature model #recognition #using
Face recognition using semi-supervised spectral feature selection (ZZ, ERH), pp. 1294–1297.
ICPRICPR-2012-ZhangHR #classification #gender #learning
Hypergraph based semi-supervised learning for gender classification (ZZ, ERH, PR), pp. 1747–1750.
KDDKDD-2012-GrosskreutzPR
An enhanced relevance criterion for more concise supervised pattern discovery (HG, DP, SR), pp. 1442–1450.
KDDKDD-2012-ShangJW #learning
Semi-supervised learning with mixed knowledge information (FS, LCJ, FW), pp. 732–740.
KDDKDD-2012-WuWCT #detection #hybrid #named #recommendation
HySAD: a semi-supervised hybrid shilling attack detector for trustworthy product recommendation (ZW, JW, JC, DT), pp. 985–993.
KDIRKDIR-2012-AbdullinN #clustering #data type #framework #learning
A Semi-supervised Learning Framework to Cluster Mixed Data Types (AA, ON), pp. 45–54.
KDIRKDIR-2012-IkebeKT #learning #predict #smarttech #using
Friendship Prediction using Semi-supervised Learning of Latent Features in Smartphone Usage Data (YI, MK, HT), pp. 199–205.
KDIRKDIR-2012-LindnerH #constraints #learning #maintenance #parsing #random
Parsing and Maintaining Bibliographic References — Semi-supervised Learning of Conditional Random Fields with Constraints (SL, WH), pp. 233–238.
MLDMMLDM-2012-EbrahimiA #approach #clustering
Semi Supervised Clustering: A Pareto Approach (JE, MSA), pp. 237–251.
MLDMMLDM-2012-SilvaA #case study #clustering
Semi-supervised Clustering: A Case Study (AS, CA), pp. 252–263.
SIGIRSIGIR-2012-Hassan #approach #modelling #web
A semi-supervised approach to modeling web search satisfaction (AH), pp. 275–284.
SACSAC-2012-HuMB #clustering #documentation
Semi-supervised document clustering with dual supervision through seeding (YH, EEM, JB), pp. 144–151.
SACSAC-2012-HuMB12a #clustering #documentation
Enhancing semi-supervised document clustering with feature supervision (YH, EEM, JB), pp. 929–936.
SACSAC-2012-NunesCM #learning #network #similarity #social
Resolving user identities over social networks through supervised learning and rich similarity features (AN, PC, BM), pp. 728–729.
ECSAECSA-2011-TibermacineZ #evolution #quality #requirements #using #web #web service
Supervising the Evolution of Web Service Orchestrations Using Quality Requirements (CT, TZ), pp. 1–16.
DACDAC-2011-LiuDPC #approximate #composition #design #set
Supervised design space exploration by compositional approximation of Pareto sets (HYL, ID, MP, LPC), pp. 399–404.
ICDARICDAR-2011-AroraN #framework #recognition
A Semi-supervised SVM Framework for Character Recognition (AA, AMN), pp. 1105–1109.
ICDARICDAR-2011-VajdaJF #approach #learning
A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort (SV, AJ, GAF), pp. 259–263.
CHICHI-2011-FiebrinkCT #evaluation #interactive #learning
Human model evaluation in interactive supervised learning (RF, PRC, DT), pp. 147–156.
HCIHCI-DDA-2011-CharfiEKM #analysis #automation #evaluation #human-computer #interactive #network #towards
Towards an Automatic Analysis of Interaction Data for HCI Evaluation Application to a Transport Network Supervision System (SC, HE, CK, FM), pp. 175–184.
HCIHCI-MIIE-2011-KamiethBS #adaptation #interactive
Adaptive Implicit Interaction for Healthy Nutrition and Food Intake Supervision (FK, AB, CS), pp. 205–212.
HCIOCSC-2011-PujariK #approach #machine learning #predict #recommendation
A Supervised Machine Learning Link Prediction Approach for Tag Recommendation (MP, RK), pp. 336–344.
ICEISICEIS-v2-2011-LinDL #case study
A Study on the Electronic Supervision Model of Drug Distribution (ZKL, ZSD, YL), pp. 378–383.
CIKMCIKM-2011-BespalovBQS #analysis #classification #n-gram #sentiment
Sentiment classification based on supervised latent n-gram analysis (DB, BB, YQ, AS), pp. 375–382.
CIKMCIKM-2011-BianC #classification #query #taxonomy
A taxonomy of local search: semi-supervised query classification driven by information needs (JB, YC), pp. 2425–2428.
CIKMCIKM-2011-DhillonSS #information management #learning #modelling #multi #predict #web
Semi-supervised multi-task learning of structured prediction models for web information extraction (PSD, SS, SKS), pp. 957–966.
CIKMCIKM-2011-KumarLB #modelling
Supervised language modeling for temporal resolution of texts (AK, ML, JB), pp. 2069–2072.
CIKMCIKM-2011-LauLBW #learning #scalability #sentiment #web
Leveraging web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons (RYKL, CLL, PB, KFW), pp. 2457–2460.
CIKMCIKM-2011-MalikMOSS #ecosystem #graph
Exploring the corporate ecosystem with a semi-supervised entity graph (HHM, IM, MOO, SS, SS), pp. 1857–1866.
CIKMCIKM-2011-SeguelaS #hybrid #recommendation
A semi-supervised hybrid system to enhance the recommendation of channels in terms of campaign roi (JS, GS), pp. 2265–2268.
CIKMCIKM-2011-SelvarajBSS #classification #dataset
Semi-supervised SVMs for classification with unknown class proportions and a small labeled dataset (SKS, BB, SS, SKS), pp. 653–662.
CIKMCIKM-2011-SilSB
Supervised matching of comments with news article segments (DKS, SHS, CB), pp. 2125–2128.
CIKMCIKM-2011-SzummerY #learning #rank
Semi-supervised learning to rank with preference regularization (MS, EY), pp. 269–278.
CIKMCIKM-2011-TariqK #feature model #performance
Fast supervised feature extraction by term discrimination information pooling (AT, AK), pp. 2233–2236.
CIKMCIKM-2011-YangZKL #how #learning #question #why
Can irrelevant data help semi-supervised learning, why and how? (HY, SZ, IK, MRL), pp. 937–946.
ECIRECIR-2011-He #classification #sentiment
Latent Sentiment Model for Weakly-Supervised Cross-Lingual Sentiment Classification (YH), pp. 214–225.
ICMLICML-2011-BrouarddS #kernel #predict
Semi-supervised Penalized Output Kernel Regression for Link Prediction (CB, FdB, MS), pp. 593–600.
ICMLICML-2011-BuffoniCGU #learning #standard
Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision (DB, CC, PG, NU), pp. 825–832.
KDDKDD-2011-ChakiCG #learning
Supervised learning for provenance-similarity of binaries (SC, CC, AG), pp. 15–23.
KDDKDD-2011-GaoLWWL #graph #metadata #ranking #scalability
Semi-supervised ranking on very large graphs with rich metadata (BG, TYL, WW, TW, HL), pp. 96–104.
KDDKDD-2011-WangLZ #analysis #keyword #rating
Latent aspect rating analysis without aspect keyword supervision (HW, YL, CZ), pp. 618–626.
KDIRKDIR-2011-ClariziaCSGN11a #classification #novel #set
A Novel Supervised Text Classifier from a Small Training Set (FC, FC, MDS, LG, PN), pp. 545–553.
KDIRKDIR-2011-Jean-LouisBFD #approach #scalability
A Weakly Supervised Approach for Large-scale Relation Extraction (LJL, RB, OF, AD), pp. 94–103.
KEODKEOD-2011-FukumotoS #classification #clustering #graph #semantics #word
Semantic Classification of Unknown Words based on Graph-based Semi-supervised Clustering (FF, YS), pp. 37–46.
MLDMMLDM-2011-Benbrahim #fuzzy
Fuzzy Semi-supervised Support Vector Machines (HB), pp. 127–139.
MLDMMLDM-2011-LahbibBL #learning #multi
Informative Variables Selection for Multi-relational Supervised Learning (DL, MB, DL), pp. 75–87.
RecSysRecSys-2011-WuCMW #detection #learning #named
Semi-SAD: applying semi-supervised learning to shilling attack detection (ZW, JC, BM, YW), pp. 289–292.
SEKESEKE-2011-CaiZWXS #approach #component #recommendation
Recommending Component by Citation: A Semi-supervised Approach for Determination (SC, YZ, LW, BX, WS), pp. 489–494.
SIGIRSIGIR-2011-LeeHWHS #dataset #graph #image #learning #multi #pipes and filters #scalability #using
Multi-layer graph-based semi-supervised learning for large-scale image datasets using mapreduce (WYL, LCH, GLW, WHH, YFS), pp. 1121–1122.
SACSAC-2011-ZhangZZZX #detection #learning #web
Harmonic functions based semi-supervised learning for web spam detection (WZ, DZ, YZ, GZ, BX), pp. 74–75.
HPCAHPCA-2011-BobbaLHW #memory management #performance
Safe and efficient supervised memory systems (JB, ML, MDH, DAW), pp. 369–380.
ICSTICST-2011-ChenCZXF #clustering #testing #using
Using semi-supervised clustering to improve regression test selection techniques (SC, ZC, ZZ, BX, YF), pp. 1–10.
DRRDRR-2010-LiuZ #detection #documentation #image #learning
Semi-supervised learning for detecting text-lines in noisy document images (ZL, HZ), pp. 1–10.
ICEISICEIS-AIDSS-2010-MoriyasuYN #learning #self #using
Supervised Learning for Agent Positioning by using Self-organizing Map (KM, TY, HN), pp. 368–372.
CIKMCIKM-2010-FujinoUN #classification #learning #robust
A robust semi-supervised classification method for transfer learning (AF, NU, MN), pp. 379–388.
CIKMCIKM-2010-MelliE #concept #identification #ontology
Supervised identification and linking of concept mentions to a domain-specific ontology (GM, ME), pp. 1717–1720.
ICMLICML-2010-BordesUW #ambiguity #learning #ranking #semantics
Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences (AB, NU, JW), pp. 103–110.
ICMLICML-2010-ChangSGR #learning
Structured Output Learning with Indirect Supervision (MWC, VS, DG, DR), pp. 199–206.
ICMLICML-2010-DillonBL #analysis #generative #learning
Asymptotic Analysis of Generative Semi-Supervised Learning (JVD, KB, GL), pp. 295–302.
ICMLICML-2010-DruckM #generative #learning #modelling #using
High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models (GD, AM), pp. 319–326.
ICMLICML-2010-GavishNC #graph #learning #multi #theory and practice
Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning (MG, BN, RRC), pp. 367–374.
ICMLICML-2010-LayB #classification #predict #using
Supervised Aggregation of Classifiers using Artificial Prediction Markets (NL, AB), pp. 591–598.
ICMLICML-2010-LiuHC #graph #learning #scalability
Large Graph Construction for Scalable Semi-Supervised Learning (WL, JH, SFC), pp. 679–686.
ICMLICML-2010-MinMYBZ
Deep Supervised t-Distributed Embedding (MRM, LvdM, ZY, AJB, ZZ), pp. 791–798.
ICPRICPR-2010-Cevikalp #distance #learning #metric #polynomial #programming
Semi-supervised Distance Metric Learning by Quadratic Programming (HC), pp. 3352–3355.
ICPRICPR-2010-ChenF #graph #learning
Semi-supervised Graph Learning: Near Strangers or Distant Relatives (WC, GF), pp. 3368–3371.
ICPRICPR-2010-GuoBC #approach #learning #using
Support Vectors Selection for Supervised Learning Using an Ensemble Approach (LG, SB, NC), pp. 37–40.
ICPRICPR-2010-HanCR10a #concept #interactive #learning #recognition #semantics
Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition (XHH, YWC, XR), pp. 3045–3048.
ICPRICPR-2010-KimuraKSNMSI #canonical #correlation #learning #named #performance
SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations (AK, HK, MS, TN, EM, HS, KI), pp. 2933–2936.
ICPRICPR-2010-KothariFSPM #standard
Transfer of Supervision for Improved Address Standardization (GK, TAF, LVS, KHP, MKM), pp. 2178–2181.
ICPRICPR-2010-LiuLH #learning #multi #representation #using
Semi-supervised Trajectory Learning Using a Multi-Scale Key Point Based Trajectory Representation (YL, XL, WH), pp. 3525–3528.
ICPRICPR-2010-Martinez-UsoPS #image #segmentation
A Semi-supervised Gaussian Mixture Model for Image Segmentation (AMU, FP, JMS), pp. 2941–2944.
ICPRICPR-2010-PengYZC #classification #segmentation #using
Retinal Blood Vessels Segmentation Using the Radial Projection and Supervised Classification (QP, XY, LZ, YmC), pp. 1489–1492.
ICPRICPR-2010-SunSHE #learning #locality #metric
Localized Supervised Metric Learning on Temporal Physiological Data (JS, DMS, JH, SE), pp. 4149–4152.
ICPRICPR-2010-ThiCZWS #modelling #recognition #using
Weakly Supervised Action Recognition Using Implicit Shape Models (THT, LC, JZ, LW, SS), pp. 3517–3520.
ICPRICPR-2010-WangHL #analysis #incremental
Boosting Incremental Semi-supervised Discriminant Analysis for Tracking (HW, XH, CLL), pp. 2748–2751.
ICPRICPR-2010-WuLW #image #learning #retrieval #using
Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble (JW, ML, CLW), pp. 3175–3178.
ICPRICPR-2010-ZhangXL #detection #video
Extracting Key Sub-trajectory Features for Supervised Tactic Detection in Sports Video (YZ, CX, HL), pp. 125–128.
KDDKDD-2010-KongY #classification #feature model #graph
Semi-supervised feature selection for graph classification (XK, PSY), pp. 793–802.
KDDKDD-2010-LiuMTLL #learning #metric #optimisation #using
Semi-supervised sparse metric learning using alternating linearization optimization (WL, SM, DT, JL, PL), pp. 1139–1148.
RecSysRecSys-2010-BenchettaraKR #approach #collaboration #machine learning #predict #recommendation
A supervised machine learning link prediction approach for academic collaboration recommendation (NB, RK, CR), pp. 253–256.
SEKESEKE-2010-XuanJRYL #automation #classification #debugging #using
Automatic Bug Triage using Semi-Supervised Text Classification (JX, HJ, ZR, JY, ZL), pp. 209–214.
SIGIRSIGIR-2010-MeijR #modelling #query #using #wiki
Supervised query modeling using wikipedia (EM, MdR), pp. 875–876.
SIGIRSIGIR-2010-MojdehC #consistency #learning #using
Semi-supervised spam filtering using aggressive consistency learning (MM, GVC), pp. 751–752.
SIGIRSIGIR-2010-ZhenY #classification #design #named
SED: supervised experimental design and its application to text classification (YZ, DYY), pp. 299–306.
DRRDRR-2009-ZhangZLT #learning
A semi-supervised learning method to classify grant-support zone in web-based medical articles (XZ, JZ, DXL, GRT), pp. 1–10.
ICDARICDAR-2009-BallS #learning #recognition
Semi-supervised Learning for Handwriting Recognition (GRB, SNS), pp. 26–30.
ICDARICDAR-2009-BharathM #clustering #framework
A Framework Based on Semi-Supervised Clustering for Discovering Unique Writing Styles (AB, SM), pp. 891–895.
ICDARICDAR-2009-FrinkenB #learning #network #recognition #word
Evaluating Retraining Rules for Semi-Supervised Learning in Neural Network Based Cursive Word Recognition (VF, HB), pp. 31–35.
ICDARICDAR-2009-PletschacherHA #clustering #documentation #framework #recognition
A New Framework for Recognition of Heavily Degraded Characters in Historical Typewritten Documents Based on Semi-Supervised Clustering (SP, JH, AA), pp. 506–510.
CIKMCIKM-2009-BaiWGCSQCW #semantics
Supervised semantic indexing (BB, JW, DG, RC, KS, YQ, OC, KQW), pp. 187–196.
CIKMCIKM-2009-BatalH #classification #using
Boosting KNN text classification accuracy by using supervised term weighting schemes (IB, MH), pp. 2041–2044.
CIKMCIKM-2009-GargS #classification #learning
Active learning in partially supervised classification (PG, SS), pp. 1783–1786.
CIKMCIKM-2009-QianNZ #multi #performance
Efficient multi-class unlabeled constrained semi-supervised SVM (MQ, FN, CZ), pp. 1665–1668.
CIKMCIKM-2009-QiuZHZ #classification #named #self #sentiment
SELC: a self-supervised model for sentiment classification (LQ, WZ, CH, KZ), pp. 929–936.
CIKMCIKM-2009-WangHLS #comprehension #learning #query #semantics #web
Semi-supervised learning of semantic classes for query understanding: from the web and for the web (YYW, RH, XL, JS), pp. 37–46.
CIKMCIKM-2009-WangWLL #named #query #ranking #summary
HyperSum: hypergraph based semi-supervised sentence ranking for query-oriented summarization (WW, FW, WL, SL), pp. 1855–1858.
ECIRECIR-2009-BaiWCG #semantics
Supervised Semantic Indexing (BB, JW, RC, DG), pp. 761–765.
ICMLICML-2009-AdamsG #learning #named #parametricity
Archipelago: nonparametric Bayesian semi-supervised learning (RPA, ZG), pp. 1–8.
ICMLICML-2009-HelleputteD #feature model #linear #modelling
Partially supervised feature selection with regularized linear models (TH, PD), pp. 409–416.
ICMLICML-2009-JebaraWC #graph #learning
Graph construction and b-matching for semi-supervised learning (TJ, JW, SFC), pp. 441–448.
ICMLICML-2009-LiKZ #learning #using
Semi-supervised learning using label mean (YFL, JTK, ZHZ), pp. 633–640.
ICMLICML-2009-RaykarYZJFVBM #learning #multi #trust
Supervised learning from multiple experts: whom to trust when everyone lies a bit (VCR, SY, LHZ, AKJ, CF, GHV, LB, LM), pp. 889–896.
ICMLICML-2009-SindhwaniML #design #nondeterminism
Uncertainty sampling and transductive experimental design for active dual supervision (VS, PM, RDL), pp. 953–960.
ICMLICML-2009-XuWS #learning #predict
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning (LX, MW, DS), pp. 1137–1144.
ICMLICML-2009-ZhangKP #learning #prototype #scalability
Prototype vector machine for large scale semi-supervised learning (KZ, JTK, BP), pp. 1233–1240.
ICMLICML-2009-ZhuAX #classification #modelling #named #topic
MedLDA: maximum margin supervised topic models for regression and classification (JZ, AA, EPX), pp. 1257–1264.
KDDKDD-2009-XueW #classification #quantifier
Quantification and semi-supervised classification methods for handling changes in class distribution (JCX, GMW), pp. 897–906.
KDDKDD-2009-XuYL #mining #using
Named entity mining from click-through data using weakly supervised latent dirichlet allocation (GX, SHY, HL), pp. 1365–1374.
KDDKDD-2009-ZhengWLL
Information theoretic regularization for semi-supervised boosting (LZ, SW, YL, CHL), pp. 1017–1026.
MLDMMLDM-2009-TronciGR
Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method (RT, GG, FR), pp. 163–177.
SIGIRSIGIR-2009-LiWA #query #random
Extracting structured information from user queries with semi-supervised conditional random fields (XL, YYW, AA), pp. 572–579.
SACSAC-2009-MaoLPCH #approach #detection #learning #multi
Semi-supervised co-training and active learning based approach for multi-view intrusion detection (CHM, HML, DP, TC, SYH), pp. 2042–2048.
CIKMCIKM-2008-ArnoldC #adaptation
Intra-document structural frequency features for semi-supervised domain adaptation (AA, WWC), pp. 1291–1300.
CIKMCIKM-2008-ChenWD #approach #clustering #documentation
A matrix-based approach for semi-supervised document co-clustering (YC, LW, MD), pp. 1523–1524.
CIKMCIKM-2008-ChenWSZ #ranking
Semi-supervised ranking aggregation (SC, FW, YS, CZ), pp. 1427–1428.
CIKMCIKM-2008-WangCZL #constraints #learning #metric
Semi-supervised metric learning by maximizing constraint margin (FW, SC, CZ, TL), pp. 1457–1458.
CIKMCIKM-2008-XuJHLK #categorisation
Semi-supervised text categorization by active search (ZX, RJ, KH, MRL, IK), pp. 1517–1518.
ECIRECIR-2008-KritharaARG #classification #documentation #fault
Semi-supervised Document Classification with a Mislabeling Error Model (AK, MRA, JMR, CG), pp. 370–381.
ICMLICML-2008-CaruanaKY #empirical #evaluation #learning
An empirical evaluation of supervised learning in high dimensions (RC, NK, AY), pp. 96–103.
ICMLICML-2008-LiLT #classification #constraints #programming
Pairwise constraint propagation by semidefinite programming for semi-supervised classification (ZL, JL, XT), pp. 576–583.
ICMLICML-2008-LoeffFR #approximate #learning #named
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning (NL, DAF, DR), pp. 600–607.
ICMLICML-2008-RanzatoS #documentation #learning #network
Semi-supervised learning of compact document representations with deep networks (MR, MS), pp. 792–799.
ICMLICML-2008-RishGCPG #linear #modelling #reduction
Closed-form supervised dimensionality reduction with generalized linear models (IR, GG, GAC, FP, GJG), pp. 832–839.
ICMLICML-2008-SokolovskaCY #learning #modelling #probability
The asymptotics of semi-supervised learning in discriminative probabilistic models (NS, OC, FY), pp. 984–991.
ICMLICML-2008-WangZ #learning #multi #on the
On multi-view active learning and the combination with semi-supervised learning (WW, ZHZ), pp. 1152–1159.
ICMLICML-2008-WestonRC #learning
Deep learning via semi-supervised embedding (JW, FR, RC), pp. 1168–1175.
ICPRICPR-2008-AdankonC #classification
Help-training for semi-supervised discriminative classifiers. Application to SVM (MMA, MC), pp. 1–4.
ICPRICPR-2008-BasakLC #learning #summary #video
Video summarization with supervised learning (JB, VL, SC), pp. 1–4.
ICPRICPR-2008-FabletLSMCB #learning #using
Weakly supervised learning using proportion-based information: An application to fisheries acoustics (RF, RL, CS, JM, PC, JMB), pp. 1–4.
ICPRICPR-2008-HuSM #categorisation #clustering #using
Categorization using semi-supervised clustering (JH, MS, AM), pp. 1–4.
ICPRICPR-2008-JradGB #constraints #learning #multi #performance
Supervised learning rule selection for multiclass decision with performance constraints (NJ, EGM, PB), pp. 1–4.
ICPRICPR-2008-LiuWBM #kernel #learning #linear
Semi-supervised learning by locally linear embedding in kernel space (RL, YW, TB, DM), pp. 1–4.
ICPRICPR-2008-NguyenBP #approach #learning #set
A supervised learning approach for imbalanced data sets (GHN, AB, SLP), pp. 1–4.
ICPRICPR-2008-SuzukiSZ #network
Supervised enhancement of lung nodules by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD) (KS, ZS, JZ), pp. 1–4.
ICPRICPR-2008-TorselloD #generative #graph #learning
Supervised learning of a generative model for edge-weighted graphs (AT, DLD), pp. 1–4.
ICPRICPR-2008-WangWCW #algorithm #clustering #learning
A clustering algorithm combine the FCM algorithm with supervised learning normal mixture model (WW, CW, XC, AW), pp. 1–4.
KDDKDD-2008-ChengT #learning
Semi-supervised learning with data calibration for long-term time series forecasting (HC, PNT), pp. 133–141.
KDDKDD-2008-SimonKZ #agile #approach #reliability #scalability #set
Semi-supervised approach to rapid and reliable labeling of large data sets (GJS, VK, ZLZ), pp. 641–649.
KDDKDD-2008-ZhaoWZ #algorithm #named #performance #virtual machine
Cuts3vm: a fast semi-supervised svm algorithm (BZ, FW, CZ), pp. 830–838.
SIGIRSIGIR-2008-MojdehC #question
Semi-supervised spam filtering: does it work? (MM, GVC), pp. 745–746.
SACSAC-2008-ChengHVL #image #reduction
Semi-supervised dimensionality reduction in image feature space (HC, KAH, KV, DL), pp. 1207–1211.
SACSAC-2008-ChidlovskiiL08a #clustering #coordination #visual notation
Semi-supervised visual clustering for spherical coordinates systems (BC, LL), pp. 891–895.
SACSAC-2008-CorreaLSM #composition #learning #network
Neural network based systems for computer-aided musical composition: supervised x unsupervised learning (DCC, ALML, JHS, JFM), pp. 1738–1742.
CAiSECAiSE-2007-BaresiGP #aspect-oriented #policy #process
Policies and Aspects for the Supervision of BPEL Processes (LB, SG, PP), pp. 340–354.
CIKMCIKM-2007-Pasca07a #query #using #web
Weakly-supervised discovery of named entities using web search queries (MP), pp. 683–690.
CIKMCIKM-2007-SongZYZD #distance #estimation #learning #metric #ranking
Ranking with semi-supervised distance metric learning and its application to housing potential estimation (YS, BZ, WJY, CZ, JD), pp. 975–978.
ICMLICML-2007-AndoZ #generative #learning
Two-view feature generation model for semi-supervised learning (RKA, TZ), pp. 25–32.
ICMLICML-2007-Azran #algorithm #learning #markov #multi #random
The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks (AA), pp. 49–56.
ICMLICML-2007-DaiY #kernel
Kernel selection forl semi-supervised kernel machines (GD, DYY), pp. 185–192.
ICMLICML-2007-HaiderBS #clustering #detection #email #streaming
Supervised clustering of streaming data for email batch detection (PH, UB, TS), pp. 345–352.
ICMLICML-2007-MannM #learning #robust #scalability
Simple, robust, scalable semi-supervised learning via expectation regularization (GSM, AM), pp. 593–600.
ICMLICML-2007-SongSGBB #dependence #estimation #feature model
Supervised feature selection via dependence estimation (LS, AJS, AG, KMB, JB), pp. 823–830.
ICMLICML-2007-ZhaoL #feature model #learning
Spectral feature selection for supervised and unsupervised learning (ZZ, HL), pp. 1151–1157.
ICMLICML-2007-ZhouX #learning #multi #on the
On the relation between multi-instance learning and semi-supervised learning (ZHZ, JMX), pp. 1167–1174.
KDDKDD-2007-DruckPMZ #classification #generative #hybrid
Semi-supervised classification with hybrid generative/discriminative methods (GD, CP, AM, XZ), pp. 280–289.
KDDKDD-2007-PanZZPSPY #mining #modelling #network
Domain-constrained semi-supervised mining of tracking models in sensor networks (RP, JZ, VWZ, JJP, DS, SJP, QY), pp. 1023–1027.
KDDKDD-2007-TangWXZ #clustering #perspective
Enhancing semi-supervised clustering: a feature projection perspective (WT, HX, SZ, JW), pp. 707–716.
MLDMMLDM-2007-EkdahlK #classification #learning #on the
On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers (ME, TK), pp. 2–16.
SACSAC-2007-Cardoso-CachopoO #categorisation #classification #using
Semi-supervised single-label text categorization using centroid-based classifiers (ACC, ALO), pp. 844–851.
CBSECBSE-2006-MauranPL #black box #distributed
Supervising Distributed Black Boxes (PM, GP, PTXL), pp. 166–181.
ICEISICEIS-AIDSS-2006-LampertiZ #incremental
Incremental Processing of Temporal Observations in Supervision and Diagnosis of Discrete-Event Systems (GL, MZ), pp. 47–57.
ECIRECIR-2006-ChakrabortiLWW #named #semantics
Sprinkling: Supervised Latent Semantic Indexing (SC, RL, NW, SNKW), pp. 510–514.
ICMLICML-2006-BrefeldS #learning
Semi-supervised learning for structured output variables (UB, TS), pp. 145–152.
ICMLICML-2006-CaruanaN #algorithm #comparison #empirical #learning
An empirical comparison of supervised learning algorithms (RC, ANM), pp. 161–168.
ICMLICML-2006-ChapelleCZ #continuation
A continuation method for semi-supervised SVMs (OC, MC, AZ), pp. 185–192.
ICMLICML-2006-RahmaniG #learning #multi #named
MISSL: multiple-instance semi-supervised learning (RR, SAG), pp. 705–712.
ICMLICML-2006-SindhwaniKC #kernel
Deterministic annealing for semi-supervised kernel machines (VS, SSK, OC), pp. 841–848.
ICMLICML-2006-Sugiyama #analysis #reduction
Local Fisher discriminant analysis for supervised dimensionality reduction (MS), pp. 905–912.
ICMLICML-2006-YangFZB #reduction
Semi-supervised nonlinear dimensionality reduction (XY, HF, HZ, JLB), pp. 1065–1072.
ICPRICPR-v1-2006-BoschMOM #approach #classification #question #what
Object and Scene Classification: what does a Supervised Approach Provide us? (AB, XM, AO, RM), pp. 773–777.
ICPRICPR-v1-2006-ConduracheA #2d #classification #image #linear #segmentation #using
Vessel Segmentation in 2D-Projection Images Using a Supervised Linear Hysteresis Classifier (AC, TA), pp. 343–346.
ICPRICPR-v1-2006-QinL #algorithm
An Improved Semi-Supervised Support Vector Machine Based Translation Algorithm for BCI Systems (JQ, YL), pp. 1240–1243.
ICPRICPR-v1-2006-YousfiACC #database #image #learning
Supervised Learning for Guiding Hierarchy Construction: Application to Osteo-Articular Medical Images Database (KY, CA, JPC, JC), pp. 484–487.
ICPRICPR-v2-2006-LefebvreLRG #classification #comparison #image #process
Supervised Image Classification by SOM Activity Map Comparison (GL, CL, JR, CG), pp. 728–731.
ICPRICPR-v2-2006-WuLZH #learning
A Semi-supervised SVM for Manifold Learning (ZW, ChL, JZ, JH), pp. 490–493.
ICPRICPR-v2-2006-ZhangR #incremental #learning
A New Data Selection Principle for Semi-Supervised Incremental Learning (RZ, AIR), pp. 780–783.
ICPRICPR-v2-2006-ZhengLY #kernel #learning #problem
Weakly Supervised Learning on Pre-image Problem in Kernel Methods (WSZ, JHL, PCY), pp. 711–715.
ICPRICPR-v3-2006-Morii #algorithm
A Generalized K-Means Algorithm with Semi-Supervised Weight Coefficients (FM), pp. 198–201.
ICPRICPR-v3-2006-QiuXT #clustering #feedback #kernel #performance #using
Efficient Relevance Feedback Using Semi-supervised Kernel-specified K-means Clustering (BQ, CX, QT), pp. 316–319.
KDDKDD-2006-WeiK #classification
Semi-supervised time series classification (LW, EJK), pp. 748–753.
KDDKDD-2006-YuYTKW #analysis #component #probability
Supervised probabilistic principal component analysis (SY, KY, VT, HPK, MW), pp. 464–473.
SIGIRSIGIR-2006-HuangZL #learning #taxonomy
Refining hierarchical taxonomy structure via semi-supervised learning (RH, ZZ, WL), pp. 653–654.
SIGIRSIGIR-2006-SindhwaniK #linear #scalability
Large scale semi-supervised linear SVMs (VS, SSK), pp. 477–484.
LOPSTRLOPSTR-2006-LeuschelCE #logic programming #online #partial evaluation #source code #using
Supervising Offline Partial Evaluation of Logic Programs Using Online Techniques (ML, SJC, DE), pp. 43–59.
SACSAC-2006-ChenJUY #detection #distributed #fault #monitoring
Combining supervised and unsupervised monitoring for fault detection in distributed computing systems (HC, GJ, CU, KY), pp. 705–709.
SACSAC-2006-GaoCT #detection
Semi-supervised outlier detection (JG, HC, PNT), pp. 635–636.
SACSAC-2006-PechenizkiyPT #feature model #learning #reduction
The impact of sample reduction on PCA-based feature extraction for supervised learning (MP, SP, AT), pp. 553–558.
CIKMCIKM-2005-XiongSK #database #learning #multi #privacy
Privacy leakage in multi-relational databases via pattern based semi-supervised learning (HX, MS, VK), pp. 355–356.
ICMLICML-2005-FinleyJ #clustering
Supervised clustering with support vector machines (TF, TJ), pp. 217–224.
ICMLICML-2005-KulisBDM #approach #clustering #graph #kernel
Semi-supervised graph clustering: a kernel approach (BK, SB, ISD, RJM), pp. 457–464.
ICMLICML-2005-Niculescu-MizilC #learning #predict
Predicting good probabilities with supervised learning (ANM, RC), pp. 625–632.
ICMLICML-2005-RayC #comparison #empirical #learning #multi
Supervised versus multiple instance learning: an empirical comparison (SR, MC), pp. 697–704.
ICMLICML-2005-SajamaO05a #modelling #reduction #using
Supervised dimensionality reduction using mixture models (S, AO), pp. 768–775.
ICMLICML-2005-SindhwaniNB #learning
Beyond the point cloud: from transductive to semi-supervised learning (VS, PN, MB), pp. 824–831.
ICMLICML-2005-ZhuL #graph #induction #learning #modelling #scalability
Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning (XZ, JDL), pp. 1052–1059.
MLDMMLDM-2005-EickRBV #assessment #clustering #distance #similarity #using
Using Clustering to Learn Distance Functions for Supervised Similarity Assessment (CFE, AR, AB, RV), pp. 120–131.
MLDMMLDM-2005-FerrandizB #graph #multi #recursion
Multivariate Discretization by Recursive Supervised Bipartition of Graph (SF, MB), pp. 253–264.
MLDMMLDM-2005-FerrandizB05a #dataset #evaluation
Supervised Evaluation of Dataset Partitions: Advantages and Practice (SF, MB), pp. 600–609.
SIGIRSIGIR-2005-Krishnan #modelling
Short comings of latent models in supervised settings (VK), pp. 625–626.
CGOCGO-2005-StephensonA #classification #predict #using
Predicting Unroll Factors Using Supervised Classification (MS, SPA), pp. 123–134.
DocEngDocEng-2004-ChidlovskiiF #documentation #learning #legacy
Supervised learning for the legacy document conversion (BC, JF), pp. 220–228.
CIKMCIKM-2004-LiO #identification #learning #music
Semi-supervised learning for music artists style identification (TL, MO), pp. 152–153.
CIKMCIKM-2004-Zhang #classification #information management
Weakly-supervised relation classification for information extraction (ZZ), pp. 581–588.
ICMLICML-2004-BilenkoBM #clustering #constraints #learning #metric
Integrating constraints and metric learning in semi-supervised clustering (MB, SB, RJM).
ICMLICML-2004-BlumLRR #learning #random #using
Semi-supervised learning using randomized mincuts (AB, JDL, MRR, RR).
ICMLICML-2004-ChangY #adaptation #clustering #linear #metric
Locally linear metric adaptation for semi-supervised clustering (HC, DYY).
ICPRICPR-v2-2004-OudotPMM #adaptation #concept #online #recognition #self #using
Self-Supervised Writer Adaptation using Perceptive Concepts: Application to On-Line Text Recognition (LO, LP, AM, MM), pp. 598–601.
ICPRICPR-v2-2004-SarkarB #automation
Decoder Banks: Versatility, Automation, and High Accuracy without Supervised Training (PS, HSB), pp. 646–649.
ICPRICPR-v3-2004-ArchambeauBPVT #classification #parametricity
Supervised Nonparametric Information Theoretic Classification (CA, TB, VP, MV, JPT), pp. 414–417.
KDDKDD-2004-BasuBM #clustering #framework #probability
A probabilistic framework for semi-supervised clustering (SB, MB, RJM), pp. 59–68.
KDDKDD-2004-CaruanaN #analysis #data mining #empirical #learning #metric #mining #performance
Data mining in metric space: an empirical analysis of supervised learning performance criteria (RC, ANM), pp. 69–78.
SACSAC-2004-SchaadM #bibliography #case study #process
Separation, review and supervision controls in the context of a credit application process: a case study of organisational control principles (AS, JDM), pp. 1380–1384.
ICMLICML-2003-CozmanCC #learning #modelling
Semi-Supervised Learning of Mixture Models (FGC, IC, MCC), pp. 99–106.
ICMLICML-2003-ZhuGL #learning #using
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (XZ, ZG, JDL), pp. 912–919.
SACSAC-2003-DeboleS #automation #categorisation
Supervised Term Weighting for Automated Text Categorization (FD, FS), pp. 784–788.
ICSEICSE-2003-SimpsonMGDM #assessment #on the #re-engineering
On The Supervision and Assessment Of Part-Time Postgraduate Software Engineering Projects (AS, AM, JG, JD, SM), pp. 628–633.
ICMLICML-2002-BasuBM #clustering
Semi-supervised Clustering by Seeding (SB, AB, RJM), pp. 27–34.
ICMLICML-2002-LiuLYL #classification #documentation
Partially Supervised Classification of Text Documents (BL, WSL, PSY, XL), pp. 387–394.
ICMLICML-2002-MusleaMK #learning #multi #robust
Active + Semi-supervised Learning = Robust Multi-View Learning (IM, SM, CAK), pp. 435–442.
ICPRICPR-v1-2002-JiangM #algorithm #detection #evaluation
Supervised Evaluation Methodology for Curvilinear Structure Detection Algorithms (XJ, DM), pp. 103–106.
ICPRICPR-v2-2002-Han #classification #using
A Supervised Classification Scheme Using Positive Boolean Function (CCH), pp. 100–103.
ICPRICPR-v2-2002-LoogG #classification #segmentation
Supervised Segmentation by Iterated Contextual Pixel Classification (ML, BvG), pp. 925–928.
ICPRICPR-v2-2002-PaclikDKK #image #segmentation
Supervised Segmentation of Textures in Backscatter Images (PP, RPWD, GMPvK, RK), pp. 490–493.
ICPRICPR-v3-2002-LicsarS #gesture #recognition
Supervised Training Based Hand Gesture Recognition System (AL, TS), pp. 999–1002.
ICPRICPR-v3-2002-Saint-JeanF #algorithm #clustering #robust
A Robust Semi-Supervised EM-Based Clustering Algorithm with a Reject Option (CSJ, CF), pp. 399–402.
SIGIRSIGIR-2002-AminiG #learning #summary
The use of unlabeled data to improve supervised learning for text summarization (MRA, PG), pp. 105–112.
SIGIRSIGIR-2002-PengHSCR #information retrieval #segmentation #self #using #word
Using self-supervised word segmentation in Chinese information retrieval (FP, XH, DS, NC, SER), pp. 349–350.
VLDBVLDB-2001-BaumgartnerFG01a #generative
Supervised Wrapper Generation with Lixto (RB, SF, GG), pp. 715–716.
KDDKDD-2001-KaltonLWY #clustering #learning
Generalized clustering, supervised learning, and data assignment (AK, PL, KW, JPY), pp. 299–304.
KDDKDD-2001-YamanishiT
Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner (KY, JiT), pp. 389–394.
SIGIRSIGIR-2001-FranzMWZ #clustering #topic
Unsupervised and Supervised Clustering for Topic Tracking (MF, JSM, TW, WJZ), pp. 310–317.
CIKMCIKM-2000-HanK #algorithm #categorisation #documentation #performance #reduction #retrieval
Fast Supervised Dimensionality Reduction Algorithm with Applications to Document Categorization & Retrieval (EHH, GK), pp. 12–19.
CIKMCIKM-2000-KimL #clustering #documentation #information management
A Semi-Supervised Document Clustering Technique for Information Organization (HjK, SgL), pp. 30–37.
ICMLICML-2000-GoldmanZ #learning
Enhancing Supervised Learning with Unlabeled Data (SAG, YZ), pp. 327–334.
ICMLICML-2000-SchuurmansS #adaptation #learning
An Adaptive Regularization Criterion for Supervised Learning (DS, FS), pp. 847–854.
ICPRICPR-v1-2000-MarlowO #generative #segmentation
Supervised Object Segmentation and Tracking for MPEG-4 VOP Generation (SM, NEO), pp. 5125–5128.
ICPRICPR-v2-2000-NgB #classification #segmentation #using
Supervised Texture Segmentation using DWT and a Modified K-NN Classifier (BWN, AB), pp. 2545–2548.
ICPRICPR-v2-2000-PerantonisPV #analysis #classification #component #paradigm #using
Supervised Principal Component Analysis Using a Smooth Classifier Paradigm (SJP, SP, VV), pp. 2109–2112.
ICPRICPR-v3-2000-VandenbrouckeMP #classification #image #segmentation
Color Image Segmentation by Supervised Pixel Classification in a Color Texture Feature Space: Application to Soccer Image Segmentation (NV, LM, JGP), pp. 3625–3628.
ICDARICDAR-1999-LebourgeoisBE #learning #using
Structure Relation between Classes for Supervised Learning using Pretopology (FL, MB, HE), pp. 33–36.
ICDARICDAR-1999-PrevostM #online #recognition
Non-supervised Determination of Allograph Sub-classes for On-line Omni-scriptor Handwriting Recognition (LP, MM), pp. 438–441.
HCIHCI-EI-1999-Furtado #approach #design #usability #user interface #using
An Approach to Improve Design and Usability of User Interfaces for Supervision Systems by Using Human Factors (EF), pp. 993–997.
KDDKDD-1999-AggarwalGY #categorisation #clustering #on the
On the Merits of Building Categorization Systems by Supervised Clustering (CCA, SCG, PSY), pp. 352–356.
EDOCEDOC-1998-FerayBDT #object-oriented
An object-oriented software bus for supervision systems, based on DCOM (AF, FB, VD, FT), pp. 263–273.
ICPRICPR-1998-FrelicotL #classification
A pretopology-based supervised pattern classifier (CF, FL), pp. 106–109.
ICPRICPR-1998-Gimelfarb #interactive #modelling #question #segmentation #what
Supervised segmentation by pairwise interactions: do Gibbs models learn what we expect? (GLG), pp. 817–819.
HCIHCI-SEC-1997-SantoniFF #adaptation #design #interface
Aid Methodology for Designing Adaptive Human Computer Interfaces for Supervision Systems (CS, EF, PF), pp. 501–504.
KDDKDD-1997-ZighedRF #learning #multi
Optimal Multiple Intervals Discretization of Continuous Attributes for Supervised Learning (DAZ, RR, FF), pp. 295–298.
ICPRICPR-1996-FrankH #approach #learning
Pretopological approach for supervised learning (FL, HE), pp. 256–260.
ICMLICML-1995-DoughertyKS
Supervised and Unsupervised Discretization of Continuous Features (JD, RK, MS), pp. 194–202.
ICMLICML-1994-LewisC #learning #nondeterminism
Heterogenous Uncertainty Sampling for Supervised Learning (DDL, JC), pp. 148–156.
ICMLICML-1993-JordanJ #approach #divide and conquer #learning #statistics
Supervised Learning and Divide-and-Conquer: A Statistical Approach (MIJ, RAJ), pp. 159–166.
ICMLML-1992-Janikow #contest #induction #learning
Combining Competition and Cooperation in Supervised Inductive Learning (CZJ), pp. 241–248.
ICMLML-1991-JordanR #learning #modelling
Internal World Models and Supervised Learning (MIJ, DER), pp. 70–74.

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