Stem supervis$ (all stems)

363 papers:

ICPC-2015-ThungLL #categorisation #fault

Active semi-supervised defect categorization (FT, XBDL, DL), pp. 60–70.

FM-2015-Lecomte #modelling #verification

Formal Virtual Modelling and Data Verification for Supervision Systems (TL), pp. 597–600.

ICEIS-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.

ICML-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.

KDD-2015-LanH #complexity #learning #multi

Reducing the Unlabeled Sample Complexity of Semi-Supervised Multi-View Learning (CL, JH), pp. 627–634.

KDD-2015-Yi0YLW #algorithm #clustering #constraints #performance

An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints (JY, LZ, TY, WL, JW), pp. 1405–1414.

KDD-2015-ZhengYX #composition #linear #modelling #topic #using

Linear Time Samplers for Supervised Topic Models using Compositional Proposals (XZ, YY, EPX), pp. 1523–1532.

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.

MLDM-2015-TreechalongRW #clustering #using

Semi-Supervised Stream Clustering Using Labeled Data Points (KT, TR, KW), pp. 281–295.

SIGIR-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.

SAC-2015-BerardiEF0 #classification #mobile #multi

Multi-store metadata-based supervised mobile app classification (GB, AE, TF, FS), pp. 585–588.

SAC-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.

SAC-2015-RegoMP #approach #detection #folksonomy #learning

A supervised learning approach to detect subsumption relations between tags in folksonomies (ASdCR, LBM, CESP), pp. 409–415.

SAC-2015-SilvaBAR #clustering #multi #prototype #using

Semi-supervised clustering using multi-assistant-prototypes to represent each cluster (WJS, MCNB, SdA, HLR), pp. 831–836.

CASE-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.

VLDB-2014-0001PK

Supervised Meta-blocking (GP, GP, GK), pp. 1929–1940.

DHM-2014-FacoettiGV

An Environment for Domestic Supervised Amblyopia Treatment (GF, AG, AV), pp. 340–350.

CIKM-2014-LengCWZL #constraints

Supervised Hashing with Soft Constraints (CL, JC, JW, XZ, HL), pp. 1851–1854.

CIKM-2014-Wen0R #precise

Enabling Precision/Recall Preferences for Semi-supervised SVM Training (ZW, RZ, KR), pp. 421–430.

CIKM-2014-ZhukovskiyGS #rank

Supervised Nested PageRank (MZ, GG, PS), pp. 1059–1068.

ICML-c1-2014-SolomonRGB #learning

Wasserstein Propagation for Semi-Supervised Learning (JS, RMR, LJG, AB), pp. 306–314.

ICML-c1-2014-ZhouT #generative #network #predict #probability

Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction (JZ, OGT), pp. 745–753.

ICML-c2-2014-ClemenconR #ranking

Anomaly Ranking as Supervised Bipartite Ranking (SC, SR), pp. 343–351.

ICML-c2-2014-FangCL #graph #learning

Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically (YF, KCCC, HWL), pp. 406–414.

ICML-c2-2014-LiZ #higher-order #learning #problem

High Order Regularization for Semi-Supervised Learning of Structured Output Problems (YL, RSZ), pp. 1368–1376.

ICML-c2-2014-SongGJMHD #learning #locality #on the

On learning to localize objects with minimal supervision (HOS, RBG, SJ, JM, ZH, TD), pp. 1611–1619.

ICML-c2-2014-XuTXR #reduction

Large-margin Weakly Supervised Dimensionality Reduction (CX, DT, CX, YR), pp. 865–873.

ICPR-2014-BertonL #graph #learning

Graph Construction Based on Labeled Instances for Semi-supervised Learning (LB, AdAL), pp. 2477–2482.

ICPR-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.

ICPR-2014-ChengZHT #learning #recognition

Semi-supervised Learning for RGB-D Object Recognition (YC, XZ, KH, TT), pp. 2377–2382.

ICPR-2014-DuDA #modelling #topic #using

Signature Matching Using Supervised Topic Models (XD, DSD, WAA), pp. 327–332.

ICPR-2014-IosifidisTP #classification #network

Semi-supervised Classification of Human Actions Based on Neural Networks (AI, AT, IP), pp. 1336–1341.

ICPR-2014-KrytheL #analysis #linear

Implicitly Constrained Semi-supervised Linear Discriminant Analysis (JHK, ML), pp. 3762–3767.

ICPR-2014-KunwarPB #network #online #recognition

Semi-supervised Online Bayesian Network Learner for Handwritten Characters Recognition (RK, UP, MB), pp. 3104–3109.

ICPR-2014-LiuYHTH #learning #recognition #visual notation

Semi-supervised Learning for Cross-Device Visual Location Recognition (PL, PY, KH, TT, HWH), pp. 2873–2878.

ICPR-2014-Ozay #image #multi #segmentation

Semi-supervised Segmentation Fusion of Multi-spectral and Aerial Images (MO), pp. 3839–3844.

ICPR-2014-PhamKC #graph #image #learning

Semi-supervised Learning on Bi-relational Graph for Image Annotation (HDP, KHK, SC), pp. 2465–2470.

ICPR-2014-QianZX #classification #multi #social

Boosted Multi-modal Supervised Latent Dirichlet Allocation for Social Event Classification (SQ, TZ, CX), pp. 1999–2004.

ICPR-2014-RehnS #locality #visual notation

Nonlinear Supervised Locality Preserving Projections for Visual Pattern Discrimination (EMR, HS), pp. 1568–1573.

ICPR-2014-RozzaMP #graph #kernel #learning #novel

A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning (AR, MM, AP), pp. 3786–3791.

ICPR-2014-SaitoAFRSGC #learning #using

Active Semi-supervised Learning Using Optimum-Path Forest (PTMS, WPA, AXF, PJdR, CTNS, JFG, MHdC), pp. 3798–3803.

ICPR-2014-WangSWB #consistency

Label Consistent Fisher Vectors for Supervised Feature Aggregation (QW, XS, MW, KLB), pp. 3588–3593.

ICPR-2014-WangT #classification

Label-Denoising Auto-encoder for Classification with Inaccurate Supervision Information (DW, XT), pp. 3648–3653.

ICPR-2014-YangN #integration #learning #multi

Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration (YY, SN), pp. 4062–4067.

ICPR-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.

KDD-2014-GaddeAO #graph #learning #using

Active semi-supervised learning using sampling theory for graph signals (AG, AA, AO), pp. 492–501.

KDD-2014-GunnemannFRS #clustering #multi #named

SMVC: semi-supervised multi-view clustering in subspace projections (SG, IF, MR, TS), pp. 253–262.

KDD-2014-TayebiEGB #embedded #learning #predict #using

Spatially embedded co-offence prediction using supervised learning (MAT, ME, UG, PLB), pp. 1789–1798.

KDD-2014-WangNH #adaptation #induction #learning #scalability

Large-scale adaptive semi-supervised learning via unified inductive and transductive model (DW, FN, HH), pp. 482–491.

KDD-2014-ZhangTMF #learning #network

Supervised deep learning with auxiliary networks (JZ, GT, YM, WF), pp. 353–361.

MLDM-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.

SIGIR-2014-HaiCCLC #bibliography #sentiment

Coarse-to-fine review selection via supervised joint aspect and sentiment model (ZH, GC, KC, WL, PC), pp. 617–626.

SIGIR-2014-HingmireC #approach #classification #topic

Topic labeled text classification: a weakly supervised approach (SH, SC), pp. 385–394.

SIGIR-2014-ZhangTZX #algorithm #recommendation

Addressing cold start in recommender systems: a semi-supervised co-training algorithm (MZ, JT, XZ, XX), pp. 73–82.

SIGIR-2014-ZhangZLG #modelling

Supervised hashing with latent factor models (PZ, WZ, WJL, MG), pp. 173–182.

SAC-2014-Zheng #matrix #using

Semi-supervised context-aware matrix factorization: using contexts in a way of “latent” factors (YZ), pp. 292–293.

SAC-2014-ZimmermannNS #adaptation #classification

Adaptive semi supervised opinion classifier with forgetting mechanism (MZ, EN, MS), pp. 805–812.

DocEng-2013-VilaresAG #classification #twitter

Supervised polarity classification of Spanish tweets based on linguistic knowledge (DV, MAA, CGR), pp. 169–172.

DRR-2013-WalkerRS #fault #modelling #topic

Evaluating supervised topic models in the presence of OCR errors (DDW, EKR, KDS).

ICDAR-2013-BougueliaBB #approach #classification #documentation #learning

A Stream-Based Semi-supervised Active Learning Approach for Document Classification (MRB, YB, AB), pp. 611–615.

ICDAR-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.

ICDAR-2013-ZhongCC #empirical #evaluation #recognition #reduction

An Empirical Evaluation of Supervised Dimensionality Reduction for Recognition (GZ, YC, MC), pp. 1315–1319.

CSMR-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.

HCI-III-2013-HeLWG #image #segmentation #using

Semi-supervised Remote Sensing Image Segmentation Using Dynamic Region Merging (NH, KL, YW, YG), pp. 153–162.

CIKM-2013-BaoCD #classification #topic

A partially supervised cross-collection topic model for cross-domain text classification (YB, NC, AD), pp. 239–248.

CIKM-2013-FangHZ #analysis #bibliography #sentiment

Exploring weakly supervised latent sentiment explanations for aspect-level review analysis (LF, MH, XZ), pp. 1057–1066.

CIKM-2013-VolkovsZ #framework

CRF framework for supervised preference aggregation (MV, RSZ), pp. 89–98.

CIKM-2013-XuXWW #automation #feedback #image #ranking

A heterogenous automatic feedback semi-supervised method for image reranking (XCX, XSX, YW, XW), pp. 999–1008.

ICML-c1-2013-BalcanBEL #learning #performance

Efficient Semi-supervised and Active Learning of Disjunctions (NB, CB, SE, YL), pp. 633–641.

ICML-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.

ICML-c3-2013-OgawaITS

Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines (KO, MI, IT, MS), pp. 897–905.

ICML-c3-2013-TarlowSCSZ #learning #probability

Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning (DT, KS, LC, IS, RSZ), pp. 199–207.

ICML-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.

KDD-2013-ChenHKB #learning #named

DTW-D: time series semi-supervised learning from a single example (YC, BH, EJK, GEAPAB), pp. 383–391.

KDD-2013-HuaCZLR #detection #named #twitter

STED: semi-supervised targeted-interest event detectionin in twitter (TH, FC, LZ, CTL, NR), pp. 1466–1469.

KDD-2013-RaederPDSP #clustering #reduction #scalability #using

Scalable supervised dimensionality reduction using clustering (TR, CP, BD, OS, FJP), pp. 1213–1221.

KDIR-KMIS-2013-VensVB #clustering

Semi-supervised Clustering with Example Clusters (CV, BV, HB), pp. 45–51.

MLDM-2013-MinhAN #algorithm #feature model

DCA Based Algorithms for Feature Selection in Semi-supervised Support Vector Machines (LHM, LTHA, MCN), pp. 528–542.

SIGIR-2013-BonnefoyBB #detection #documentation

A weakly-supervised detection of entity central documents in a stream (LB, VB, PB), pp. 769–772.

SAC-2013-AkritidisB #algorithm #classification #machine learning #research

A supervised machine learning classification algorithm for research articles (LA, PB), pp. 115–120.

SAC-2013-HassanzadehN #algorithm #detection #graph

A semi-supervised graph-based algorithm for detecting outliers in online-social-networks (RH, RN), pp. 577–582.

ASE-2012-LuCC #fault #learning #predict #reduction #using

Software defect prediction using semi-supervised learning with dimension reduction (HL, BC, MC), pp. 314–317.

CASE-2012-Markovski #framework

A process-theoretic state-based framework for live supervision (JM), pp. 680–685.

DocEng-2012-HuMBL #clustering #documentation #personalisation

Personalized document clustering with dual supervision (YH, EEM, JB, SL), pp. 161–170.

ICSM-2012-HallWM #composition

Supervised software modularisation (MH, NW, PM), pp. 472–481.

IFM-2012-MarkovskiBB #component #requirements

Partially-Supervised Plants: Embedding Control Requirements in Plant Components (JM, DAvB, JCMB), pp. 253–267.

CIKM-2012-BlessingS #complexity

Crosslingual distant supervision for extracting relations of different complexity (AB, HS), pp. 1123–1132.

CIKM-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.

CIKM-2012-MaoHYL #integration #modelling #topic

Hierarchical topic integration through semi-supervised hierarchical topic modeling (XM, JH, HY, XL), pp. 1612–1616.

CIKM-2012-QuanzH #generative #learning #multi #named

CoNet: feature generation for multi-view semi-supervised learning with partially observed views (BQ, JH), pp. 1273–1282.

CIKM-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.

ICML-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.

ICML-2012-JoulinB #classification

A convex relaxation for weakly supervised classifiers (AJ, FRB), p. 171.

ICML-2012-LinXWZ #learning

Total Variation and Euler’s Elastica for Supervised Learning (TL, HX, LW, HZ), p. 82.

ICML-2012-McDowellA #classification #hybrid

Semi-Supervised Collective Classification via Hybrid Label Regularization (LM, DWA), p. 162.

ICML-2012-NiuDYS #learning #metric

Information-theoretic Semi-supervised Metric Learning via Entropy Regularization (GN, BD, MY, MS), p. 136.

ICML-2012-PlessisS #learning

Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching (MCdP, MS), p. 159.

ICPR-2012-BaiXP #classification #image

Classification oriented semi-supervised band selection for hyperspectral images (JB, SX, CP), pp. 1888–1891.

ICPR-2012-BespalovQBS #classification #image #scalability #using

Large-scale image classification using supervised spatial encoder (DB, YQ, BB, AS), pp. 581–584.

ICPR-2012-CermanH #learning #problem

Tracking with context as a semi-supervised learning and labeling problem (LC, VH), pp. 2124–2127.

ICPR-2012-CourtyAL #approach #classification #image

A classwise supervised ordering approach for morphology based hyperspectral image classification (NC, EA, SL), pp. 1997–2000.

ICPR-2012-DinhDL #case study #difference #representation

A study on semi-supervised dissimilarity representation (VCD, RPWD, ML), pp. 2861–2864.

ICPR-2012-HidoK #graph #learning #similarity

Hash-based structural similarity for semi-supervised Learning on attribute graphs (SH, HK), pp. 3009–3012.

ICPR-2012-LiL12a #image #invariant #segmentation

Scale-invariant sampling for supervised image segmentation (YL, ML), pp. 1399–1402.

ICPR-2012-LiWBL #adaptation #algorithm #fault

Semi-supervised adaptive parzen Gentleboost algorithm for fault diagnosis (CL, ZW, SB, ZL), pp. 2290–2293.

ICPR-2012-PourdamghaniRZ #estimation #graph #learning #metric

Metric learning for graph based semi-supervised human pose estimation (NP, HRR, MZ), pp. 3386–3389.

ICPR-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.

ICPR-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.

ICPR-2012-ZhangH #feature model #recognition #using

Face recognition using semi-supervised spectral feature selection (ZZ, ERH), pp. 1294–1297.

ICPR-2012-ZhangHR #classification #gender #learning

Hypergraph based semi-supervised learning for gender classification (ZZ, ERH, PR), pp. 1747–1750.

KDD-2012-GrosskreutzPR

An enhanced relevance criterion for more concise supervised pattern discovery (HG, DP, SR), pp. 1442–1450.

KDD-2012-ShangJW #learning

Semi-supervised learning with mixed knowledge information (FS, LCJ, FW), pp. 732–740.

KDD-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.

KDIR-2012-AbdullinN #clustering #data type #framework #learning

A Semi-supervised Learning Framework to Cluster Mixed Data Types (AA, ON), pp. 45–54.

KDIR-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.

KDIR-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.

MLDM-2012-EbrahimiA #approach #clustering

Semi Supervised Clustering: A Pareto Approach (JE, MSA), pp. 237–251.

MLDM-2012-SilvaA #case study #clustering

Semi-supervised Clustering: A Case Study (AS, CA), pp. 252–263.

SIGIR-2012-Hassan #approach #modelling #web

A semi-supervised approach to modeling web search satisfaction (AH), pp. 275–284.

SAC-2012-HuMB #clustering #documentation

Semi-supervised document clustering with dual supervision through seeding (YH, EEM, JB), pp. 144–151.

SAC-2012-HuMB12a #clustering #documentation

Enhancing semi-supervised document clustering with feature supervision (YH, EEM, JB), pp. 929–936.

SAC-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.

ECSA-2011-TibermacineZ #evolution #quality #requirements #using #web #web service

Supervising the Evolution of Web Service Orchestrations Using Quality Requirements (CT, TZ), pp. 1–16.

DAC-2011-LiuDPC #approximate #composition #design #set

Supervised design space exploration by compositional approximation of Pareto sets (HYL, ID, MP, LPC), pp. 399–404.

ICDAR-2011-AroraN #framework #recognition

A Semi-supervised SVM Framework for Character Recognition (AA, AMN), pp. 1105–1109.

ICDAR-2011-VajdaJF #approach #learning

A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort (SV, AJ, GAF), pp. 259–263.

CHI-2011-FiebrinkCT #evaluation #interactive #learning

Human model evaluation in interactive supervised learning (RF, PRC, DT), pp. 147–156.

HCI-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.

HCI-MIIE-2011-KamiethBS #adaptation #interactive

Adaptive Implicit Interaction for Healthy Nutrition and Food Intake Supervision (FK, AB, CS), pp. 205–212.

OCSC-2011-PujariK #approach #machine learning #predict #recommendation

A Supervised Machine Learning Link Prediction Approach for Tag Recommendation (MP, RK), pp. 336–344.

ICEIS-v2-2011-LinDL #case study

A Study on the Electronic Supervision Model of Drug Distribution (ZKL, ZSD, YL), pp. 378–383.

CIKM-2011-BespalovBQS #analysis #classification #n-gram #sentiment

Sentiment classification based on supervised latent n-gram analysis (DB, BB, YQ, AS), pp. 375–382.

CIKM-2011-BianC #classification #query #taxonomy

A taxonomy of local search: semi-supervised query classification driven by information needs (JB, YC), pp. 2425–2428.

CIKM-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.

CIKM-2011-KumarLB #modelling

Supervised language modeling for temporal resolution of texts (AK, ML, JB), pp. 2069–2072.

CIKM-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.

CIKM-2011-MalikMOSS #ecosystem #graph

Exploring the corporate ecosystem with a semi-supervised entity graph (HHM, IM, MOO, SS, SS), pp. 1857–1866.

CIKM-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.

CIKM-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.

CIKM-2011-SilSB

Supervised matching of comments with news article segments (DKS, SHS, CB), pp. 2125–2128.

CIKM-2011-SzummerY #learning #rank

Semi-supervised learning to rank with preference regularization (MS, EY), pp. 269–278.

CIKM-2011-TariqK #feature model #performance

Fast supervised feature extraction by term discrimination information pooling (AT, AK), pp. 2233–2236.

CIKM-2011-YangZKL #how #learning #question #why

Can irrelevant data help semi-supervised learning, why and how? (HY, SZ, IK, MRL), pp. 937–946.

ECIR-2011-He #classification #sentiment

Latent Sentiment Model for Weakly-Supervised Cross-Lingual Sentiment Classification (YH), pp. 214–225.

ICML-2011-BrouarddS #kernel #predict

Semi-supervised Penalized Output Kernel Regression for Link Prediction (CB, FdB, MS), pp. 593–600.

ICML-2011-BuffoniCGU #learning #standard

Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision (DB, CC, PG, NU), pp. 825–832.

KDD-2011-ChakiCG #learning

Supervised learning for provenance-similarity of binaries (SC, CC, AG), pp. 15–23.

KDD-2011-GaoLWWL #graph #metadata #ranking #scalability

Semi-supervised ranking on very large graphs with rich metadata (BG, TYL, WW, TW, HL), pp. 96–104.

KDD-2011-WangLZ #analysis #keyword #rating

Latent aspect rating analysis without aspect keyword supervision (HW, YL, CZ), pp. 618–626.

KDIR-2011-ClariziaCSGN11a #classification #novel #set

A Novel Supervised Text Classifier from a Small Training Set (FC, FC, MDS, LG, PN), pp. 545–553.

KDIR-2011-Jean-LouisBFD #approach #scalability

A Weakly Supervised Approach for Large-scale Relation Extraction (LJL, RB, OF, AD), pp. 94–103.

KEOD-2011-FukumotoS #classification #clustering #graph #semantics #word

Semantic Classification of Unknown Words based on Graph-based Semi-supervised Clustering (FF, YS), pp. 37–46.

MLDM-2011-Benbrahim #fuzzy

Fuzzy Semi-supervised Support Vector Machines (HB), pp. 127–139.

MLDM-2011-LahbibBL #learning #multi

Informative Variables Selection for Multi-relational Supervised Learning (DL, MB, DL), pp. 75–87.

RecSys-2011-WuCMW #detection #learning #named

Semi-SAD: applying semi-supervised learning to shilling attack detection (ZW, JC, BM, YW), pp. 289–292.

SEKE-2011-CaiZWXS #approach #component #recommendation

Recommending Component by Citation: A Semi-supervised Approach for Determination (SC, YZ, LW, BX, WS), pp. 489–494.

SIGIR-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.

SAC-2011-ZhangZZZX #detection #learning #web

Harmonic functions based semi-supervised learning for web spam detection (WZ, DZ, YZ, GZ, BX), pp. 74–75.

HPCA-2011-BobbaLHW #memory management #performance

Safe and efficient supervised memory systems (JB, ML, MDH, DAW), pp. 369–380.

ICST-2011-ChenCZXF #clustering #testing #using

Using semi-supervised clustering to improve regression test selection techniques (SC, ZC, ZZ, BX, YF), pp. 1–10.

DRR-2010-LiuZ #detection #documentation #image #learning

Semi-supervised learning for detecting text-lines in noisy document images (ZL, HZ), pp. 1–10.

ICEIS-AIDSS-2010-MoriyasuYN #learning #self #using

Supervised Learning for Agent Positioning by using Self-organizing Map (KM, TY, HN), pp. 368–372.

CIKM-2010-FujinoUN #classification #learning #robust

A robust semi-supervised classification method for transfer learning (AF, NU, MN), pp. 379–388.

CIKM-2010-MelliE #concept #identification #ontology

Supervised identification and linking of concept mentions to a domain-specific ontology (GM, ME), pp. 1717–1720.

ICML-2010-BordesUW #ambiguity #learning #ranking #semantics

Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences (AB, NU, JW), pp. 103–110.

ICML-2010-ChangSGR #learning

Structured Output Learning with Indirect Supervision (MWC, VS, DG, DR), pp. 199–206.

ICML-2010-DillonBL #analysis #generative #learning

Asymptotic Analysis of Generative Semi-Supervised Learning (JVD, KB, GL), pp. 295–302.

ICML-2010-DruckM #generative #learning #modelling #using

High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models (GD, AM), pp. 319–326.

ICML-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.

ICML-2010-LayB #classification #predict #using

Supervised Aggregation of Classifiers using Artificial Prediction Markets (NL, AB), pp. 591–598.

ICML-2010-LiuHC #graph #learning #scalability

Large Graph Construction for Scalable Semi-Supervised Learning (WL, JH, SFC), pp. 679–686.

ICML-2010-MinMYBZ

Deep Supervised t-Distributed Embedding (MRM, LvdM, ZY, AJB, ZZ), pp. 791–798.

ICPR-2010-Cevikalp #distance #learning #metric #polynomial #programming

Semi-supervised Distance Metric Learning by Quadratic Programming (HC), pp. 3352–3355.

ICPR-2010-ChenF #graph #learning

Semi-supervised Graph Learning: Near Strangers or Distant Relatives (WC, GF), pp. 3368–3371.

ICPR-2010-GuoBC #approach #learning #using

Support Vectors Selection for Supervised Learning Using an Ensemble Approach (LG, SB, NC), pp. 37–40.

ICPR-2010-HanCR10a #concept #interactive #learning #recognition #semantics

Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition (XHH, YWC, XR), pp. 3045–3048.

ICPR-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.

ICPR-2010-KothariFSPM #standard

Transfer of Supervision for Improved Address Standardization (GK, TAF, LVS, KHP, MKM), pp. 2178–2181.

ICPR-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.

ICPR-2010-Martinez-UsoPS #image #segmentation

A Semi-supervised Gaussian Mixture Model for Image Segmentation (AMU, FP, JMS), pp. 2941–2944.

ICPR-2010-PengYZC #classification #segmentation #using

Retinal Blood Vessels Segmentation Using the Radial Projection and Supervised Classification (QP, XY, LZ, YmC), pp. 1489–1492.

ICPR-2010-SunSHE #learning #locality #metric

Localized Supervised Metric Learning on Temporal Physiological Data (JS, DMS, JH, SE), pp. 4149–4152.

ICPR-2010-ThiCZWS #modelling #recognition #using

Weakly Supervised Action Recognition Using Implicit Shape Models (THT, LC, JZ, LW, SS), pp. 3517–3520.

ICPR-2010-WangHL #analysis #incremental

Boosting Incremental Semi-supervised Discriminant Analysis for Tracking (HW, XH, CLL), pp. 2748–2751.

ICPR-2010-WuLW #image #learning #retrieval #using

Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble (JW, ML, CLW), pp. 3175–3178.

ICPR-2010-ZhangXL #detection #video

Extracting Key Sub-trajectory Features for Supervised Tactic Detection in Sports Video (YZ, CX, HL), pp. 125–128.

KDD-2010-KongY #classification #feature model #graph

Semi-supervised feature selection for graph classification (XK, PSY), pp. 793–802.

KDD-2010-LiuMTLL #learning #metric #optimisation #using

Semi-supervised sparse metric learning using alternating linearization optimization (WL, SM, DT, JL, PL), pp. 1139–1148.

RecSys-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.

SEKE-2010-XuanJRYL #automation #classification #debugging #using

Automatic Bug Triage using Semi-Supervised Text Classification (JX, HJ, ZR, JY, ZL), pp. 209–214.

SIGIR-2010-MeijR #modelling #query #using #wiki

Supervised query modeling using wikipedia (EM, MdR), pp. 875–876.

SIGIR-2010-MojdehC #consistency #learning #using

Semi-supervised spam filtering using aggressive consistency learning (MM, GVC), pp. 751–752.

SIGIR-2010-ZhenY #classification #design #named

SED: supervised experimental design and its application to text classification (YZ, DYY), pp. 299–306.

DRR-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.

ICDAR-2009-BallS #learning #recognition

Semi-supervised Learning for Handwriting Recognition (GRB, SNS), pp. 26–30.

ICDAR-2009-BharathM #clustering #framework

A Framework Based on Semi-Supervised Clustering for Discovering Unique Writing Styles (AB, SM), pp. 891–895.

ICDAR-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.

ICDAR-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.

CIKM-2009-BaiWGCSQCW #semantics

Supervised semantic indexing (BB, JW, DG, RC, KS, YQ, OC, KQW), pp. 187–196.

CIKM-2009-BatalH #classification #using

Boosting KNN text classification accuracy by using supervised term weighting schemes (IB, MH), pp. 2041–2044.

CIKM-2009-GargS #classification #learning

Active learning in partially supervised classification (PG, SS), pp. 1783–1786.

CIKM-2009-QianNZ #multi #performance

Efficient multi-class unlabeled constrained semi-supervised SVM (MQ, FN, CZ), pp. 1665–1668.

CIKM-2009-QiuZHZ #classification #named #self #sentiment

SELC: a self-supervised model for sentiment classification (LQ, WZ, CH, KZ), pp. 929–936.

CIKM-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.

CIKM-2009-WangWLL #named #query #ranking #summary

HyperSum: hypergraph based semi-supervised sentence ranking for query-oriented summarization (WW, FW, WL, SL), pp. 1855–1858.

ECIR-2009-BaiWCG #semantics

Supervised Semantic Indexing (BB, JW, RC, DG), pp. 761–765.

ICML-2009-AdamsG #learning #named #parametricity

Archipelago: nonparametric Bayesian semi-supervised learning (RPA, ZG), pp. 1–8.

ICML-2009-HelleputteD #feature model #linear #modelling

Partially supervised feature selection with regularized linear models (TH, PD), pp. 409–416.

ICML-2009-JebaraWC #graph #learning

Graph construction and b-matching for semi-supervised learning (TJ, JW, SFC), pp. 441–448.

ICML-2009-LiKZ #learning #using

Semi-supervised learning using label mean (YFL, JTK, ZHZ), pp. 633–640.

ICML-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.

ICML-2009-SindhwaniML #design #nondeterminism

Uncertainty sampling and transductive experimental design for active dual supervision (VS, PM, RDL), pp. 953–960.

ICML-2009-XuWS #learning #predict

Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning (LX, MW, DS), pp. 1137–1144.

ICML-2009-ZhangKP #learning #prototype #scalability

Prototype vector machine for large scale semi-supervised learning (KZ, JTK, BP), pp. 1233–1240.

ICML-2009-ZhuAX #classification #modelling #named #topic

MedLDA: maximum margin supervised topic models for regression and classification (JZ, AA, EPX), pp. 1257–1264.

KDD-2009-XueW #classification #quantifier

Quantification and semi-supervised classification methods for handling changes in class distribution (JCX, GMW), pp. 897–906.

KDD-2009-XuYL #mining #using

Named entity mining from click-through data using weakly supervised latent dirichlet allocation (GX, SHY, HL), pp. 1365–1374.

KDD-2009-ZhengWLL

Information theoretic regularization for semi-supervised boosting (LZ, SW, YL, CHL), pp. 1017–1026.

MLDM-2009-TronciGR

Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method (RT, GG, FR), pp. 163–177.

SIGIR-2009-LiWA #query #random

Extracting structured information from user queries with semi-supervised conditional random fields (XL, YYW, AA), pp. 572–579.

SAC-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.

CIKM-2008-ArnoldC #adaptation

Intra-document structural frequency features for semi-supervised domain adaptation (AA, WWC), pp. 1291–1300.

CIKM-2008-ChenWD #approach #clustering #documentation

A matrix-based approach for semi-supervised document co-clustering (YC, LW, MD), pp. 1523–1524.

CIKM-2008-ChenWSZ #ranking

Semi-supervised ranking aggregation (SC, FW, YS, CZ), pp. 1427–1428.

CIKM-2008-WangCZL #constraints #learning #metric

Semi-supervised metric learning by maximizing constraint margin (FW, SC, CZ, TL), pp. 1457–1458.

CIKM-2008-XuJHLK #categorisation

Semi-supervised text categorization by active search (ZX, RJ, KH, MRL, IK), pp. 1517–1518.

ECIR-2008-KritharaARG #classification #documentation #fault

Semi-supervised Document Classification with a Mislabeling Error Model (AK, MRA, JMR, CG), pp. 370–381.

ICML-2008-CaruanaKY #empirical #evaluation #learning

An empirical evaluation of supervised learning in high dimensions (RC, NK, AY), pp. 96–103.

ICML-2008-LiLT #classification #constraints #programming

Pairwise constraint propagation by semidefinite programming for semi-supervised classification (ZL, JL, XT), pp. 576–583.

ICML-2008-LoeffFR #approximate #learning #named

ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning (NL, DAF, DR), pp. 600–607.

ICML-2008-RanzatoS #documentation #learning #network

Semi-supervised learning of compact document representations with deep networks (MR, MS), pp. 792–799.

ICML-2008-RishGCPG #linear #modelling #reduction

Closed-form supervised dimensionality reduction with generalized linear models (IR, GG, GAC, FP, GJG), pp. 832–839.

ICML-2008-SokolovskaCY #learning #modelling #probability

The asymptotics of semi-supervised learning in discriminative probabilistic models (NS, OC, FY), pp. 984–991.

ICML-2008-WangZ #learning #multi #on the

On multi-view active learning and the combination with semi-supervised learning (WW, ZHZ), pp. 1152–1159.

ICML-2008-WestonRC #learning

Deep learning via semi-supervised embedding (JW, FR, RC), pp. 1168–1175.

ICPR-2008-AdankonC #classification

Help-training for semi-supervised discriminative classifiers. Application to SVM (MMA, MC), pp. 1–4.

ICPR-2008-BasakLC #learning #summary #video

Video summarization with supervised learning (JB, VL, SC), pp. 1–4.

ICPR-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.

ICPR-2008-HuSM #categorisation #clustering #using

Categorization using semi-supervised clustering (JH, MS, AM), pp. 1–4.

ICPR-2008-JradGB #constraints #learning #multi #performance

Supervised learning rule selection for multiclass decision with performance constraints (NJ, EGM, PB), pp. 1–4.

ICPR-2008-LiuWBM #kernel #learning #linear

Semi-supervised learning by locally linear embedding in kernel space (RL, YW, TB, DM), pp. 1–4.

ICPR-2008-NguyenBP #approach #learning #set

A supervised learning approach for imbalanced data sets (GHN, AB, SLP), pp. 1–4.

ICPR-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.

ICPR-2008-TorselloD #generative #graph #learning

Supervised learning of a generative model for edge-weighted graphs (AT, DLD), pp. 1–4.

ICPR-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.

KDD-2008-ChengT #learning

Semi-supervised learning with data calibration for long-term time series forecasting (HC, PNT), pp. 133–141.

KDD-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.

KDD-2008-ZhaoWZ #algorithm #named #performance #virtual machine

Cuts3vm: a fast semi-supervised svm algorithm (BZ, FW, CZ), pp. 830–838.

SIGIR-2008-MojdehC #question

Semi-supervised spam filtering: does it work? (MM, GVC), pp. 745–746.

SAC-2008-ChengHVL #image #reduction

Semi-supervised dimensionality reduction in image feature space (HC, KAH, KV, DL), pp. 1207–1211.

SAC-2008-ChidlovskiiL08a #clustering #coordination #visual notation

Semi-supervised visual clustering for spherical coordinates systems (BC, LL), pp. 891–895.

SAC-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.

CAiSE-2007-BaresiGP #aspect-oriented #policy #process

Policies and Aspects for the Supervision of BPEL Processes (LB, SG, PP), pp. 340–354.

CIKM-2007-Pasca07a #query #using #web

Weakly-supervised discovery of named entities using web search queries (MP), pp. 683–690.

CIKM-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.

ICML-2007-AndoZ #generative #learning

Two-view feature generation model for semi-supervised learning (RKA, TZ), pp. 25–32.

ICML-2007-Azran #algorithm #learning #markov #multi #random

The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks (AA), pp. 49–56.

ICML-2007-DaiY #kernel

Kernel selection forl semi-supervised kernel machines (GD, DYY), pp. 185–192.

ICML-2007-HaiderBS #clustering #detection #email #streaming

Supervised clustering of streaming data for email batch detection (PH, UB, TS), pp. 345–352.

ICML-2007-MannM #learning #robust #scalability

Simple, robust, scalable semi-supervised learning via expectation regularization (GSM, AM), pp. 593–600.

ICML-2007-SongSGBB #dependence #estimation #feature model

Supervised feature selection via dependence estimation (LS, AJS, AG, KMB, JB), pp. 823–830.

ICML-2007-ZhaoL #feature model #learning

Spectral feature selection for supervised and unsupervised learning (ZZ, HL), pp. 1151–1157.

ICML-2007-ZhouX #learning #multi #on the

On the relation between multi-instance learning and semi-supervised learning (ZHZ, JMX), pp. 1167–1174.

KDD-2007-DruckPMZ #classification #generative #hybrid

Semi-supervised classification with hybrid generative/discriminative methods (GD, CP, AM, XZ), pp. 280–289.

KDD-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.

KDD-2007-TangWXZ #clustering #perspective

Enhancing semi-supervised clustering: a feature projection perspective (WT, HX, SZ, JW), pp. 707–716.

MLDM-2007-EkdahlK #classification #learning #on the

On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers (ME, TK), pp. 2–16.

SAC-2007-Cardoso-CachopoO #categorisation #classification #using

Semi-supervised single-label text categorization using centroid-based classifiers (ACC, ALO), pp. 844–851.

CBSE-2006-MauranPL #black box #distributed

Supervising Distributed Black Boxes (PM, GP, PTXL), pp. 166–181.

ICEIS-AIDSS-2006-LampertiZ #incremental

Incremental Processing of Temporal Observations in Supervision and Diagnosis of Discrete-Event Systems (GL, MZ), pp. 47–57.

ECIR-2006-ChakrabortiLWW #named #semantics

Sprinkling: Supervised Latent Semantic Indexing (SC, RL, NW, SNKW), pp. 510–514.

ICML-2006-BrefeldS #learning

Semi-supervised learning for structured output variables (UB, TS), pp. 145–152.

ICML-2006-CaruanaN #algorithm #comparison #empirical #learning

An empirical comparison of supervised learning algorithms (RC, ANM), pp. 161–168.

ICML-2006-ChapelleCZ #continuation

A continuation method for semi-supervised SVMs (OC, MC, AZ), pp. 185–192.

ICML-2006-RahmaniG #learning #multi #named

MISSL: multiple-instance semi-supervised learning (RR, SAG), pp. 705–712.

ICML-2006-SindhwaniKC #kernel

Deterministic annealing for semi-supervised kernel machines (VS, SSK, OC), pp. 841–848.

ICML-2006-Sugiyama #analysis #reduction

Local Fisher discriminant analysis for supervised dimensionality reduction (MS), pp. 905–912.

ICML-2006-YangFZB #reduction

Semi-supervised nonlinear dimensionality reduction (XY, HF, HZ, JLB), pp. 1065–1072.

ICPR-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.

ICPR-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.

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-YousfiACC #database #image #learning

Supervised Learning for Guiding Hierarchy Construction: Application to Osteo-Articular Medical Images Database (KY, CA, JPC, JC), pp. 484–487.

ICPR-v2-2006-LefebvreLRG #classification #comparison #image #process

Supervised Image Classification by SOM Activity Map Comparison (GL, CL, JR, CG), pp. 728–731.

ICPR-v2-2006-WuLZH #learning

A Semi-supervised SVM for Manifold Learning (ZW, ChL, JZ, JH), pp. 490–493.

ICPR-v2-2006-ZhangR #incremental #learning

A New Data Selection Principle for Semi-Supervised Incremental Learning (RZ, AIR), pp. 780–783.

ICPR-v2-2006-ZhengLY #kernel #learning #problem

Weakly Supervised Learning on Pre-image Problem in Kernel Methods (WSZ, JHL, PCY), pp. 711–715.

ICPR-v3-2006-Morii #algorithm

A Generalized K-Means Algorithm with Semi-Supervised Weight Coefficients (FM), pp. 198–201.

ICPR-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.

KDD-2006-WeiK #classification

Semi-supervised time series classification (LW, EJK), pp. 748–753.

KDD-2006-YuYTKW #analysis #component #probability

Supervised probabilistic principal component analysis (SY, KY, VT, HPK, MW), pp. 464–473.

SIGIR-2006-HuangZL #learning #taxonomy

Refining hierarchical taxonomy structure via semi-supervised learning (RH, ZZ, WL), pp. 653–654.

SIGIR-2006-SindhwaniK #linear #scalability

Large scale semi-supervised linear SVMs (VS, SSK), pp. 477–484.

LOPSTR-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.

SAC-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.

SAC-2006-GaoCT #detection

Semi-supervised outlier detection (JG, HC, PNT), pp. 635–636.

SAC-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.

CIKM-2005-XiongSK #database #learning #multi #privacy

Privacy leakage in multi-relational databases via pattern based semi-supervised learning (HX, MS, VK), pp. 355–356.

ICML-2005-FinleyJ #clustering

Supervised clustering with support vector machines (TF, TJ), pp. 217–224.

ICML-2005-KulisBDM #approach #clustering #graph #kernel

Semi-supervised graph clustering: a kernel approach (BK, SB, ISD, RJM), pp. 457–464.

ICML-2005-Niculescu-MizilC #learning #predict

Predicting good probabilities with supervised learning (ANM, RC), pp. 625–632.

ICML-2005-RayC #comparison #empirical #learning #multi

Supervised versus multiple instance learning: an empirical comparison (SR, MC), pp. 697–704.

ICML-2005-SajamaO05a #modelling #reduction #using

Supervised dimensionality reduction using mixture models (S, AO), pp. 768–775.

ICML-2005-SindhwaniNB #learning

Beyond the point cloud: from transductive to semi-supervised learning (VS, PN, MB), pp. 824–831.

ICML-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.

MLDM-2005-EickRBV #assessment #clustering #distance #similarity #using

Using Clustering to Learn Distance Functions for Supervised Similarity Assessment (CFE, AR, AB, RV), pp. 120–131.

MLDM-2005-FerrandizB #graph #multi #recursion

Multivariate Discretization by Recursive Supervised Bipartition of Graph (SF, MB), pp. 253–264.

MLDM-2005-FerrandizB05a #dataset #evaluation

Supervised Evaluation of Dataset Partitions: Advantages and Practice (SF, MB), pp. 600–609.

SIGIR-2005-Krishnan #modelling

Short comings of latent models in supervised settings (VK), pp. 625–626.

CGO-2005-StephensonA #classification #predict #using

Predicting Unroll Factors Using Supervised Classification (MS, SPA), pp. 123–134.

DocEng-2004-ChidlovskiiF #documentation #learning #legacy

Supervised learning for the legacy document conversion (BC, JF), pp. 220–228.

CIKM-2004-LiO #identification #learning #music

Semi-supervised learning for music artists style identification (TL, MO), pp. 152–153.

CIKM-2004-Zhang #classification #information management

Weakly-supervised relation classification for information extraction (ZZ), pp. 581–588.

ICML-2004-BilenkoBM #clustering #constraints #learning #metric

Integrating constraints and metric learning in semi-supervised clustering (MB, SB, RJM).

ICML-2004-BlumLRR #learning #random #using

Semi-supervised learning using randomized mincuts (AB, JDL, MRR, RR).

ICML-2004-ChangY #adaptation #clustering #linear #metric

Locally linear metric adaptation for semi-supervised clustering (HC, DYY).

ICPR-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.

ICPR-v2-2004-SarkarB #automation

Decoder Banks: Versatility, Automation, and High Accuracy without Supervised Training (PS, HSB), pp. 646–649.

ICPR-v3-2004-ArchambeauBPVT #classification #parametricity

Supervised Nonparametric Information Theoretic Classification (CA, TB, VP, MV, JPT), pp. 414–417.

KDD-2004-BasuBM #clustering #framework #probability

A probabilistic framework for semi-supervised clustering (SB, MB, RJM), pp. 59–68.

KDD-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.

SAC-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.

ICML-2003-CozmanCC #learning #modelling

Semi-Supervised Learning of Mixture Models (FGC, IC, MCC), pp. 99–106.

ICML-2003-ZhuGL #learning #using

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (XZ, ZG, JDL), pp. 912–919.

SAC-2003-DeboleS #automation #categorisation

Supervised Term Weighting for Automated Text Categorization (FD, FS), pp. 784–788.

ICSE-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.

ICML-2002-BasuBM #clustering

Semi-supervised Clustering by Seeding (SB, AB, RJM), pp. 27–34.

ICML-2002-LiuLYL #classification #documentation

Partially Supervised Classification of Text Documents (BL, WSL, PSY, XL), pp. 387–394.

ICML-2002-MusleaMK #learning #multi #robust

Active + Semi-supervised Learning = Robust Multi-View Learning (IM, SM, CAK), pp. 435–442.

ICPR-v1-2002-JiangM #algorithm #detection #evaluation

Supervised Evaluation Methodology for Curvilinear Structure Detection Algorithms (XJ, DM), pp. 103–106.

ICPR-v2-2002-Han #classification #using

A Supervised Classification Scheme Using Positive Boolean Function (CCH), pp. 100–103.

ICPR-v2-2002-LoogG #classification #segmentation

Supervised Segmentation by Iterated Contextual Pixel Classification (ML, BvG), pp. 925–928.

ICPR-v2-2002-PaclikDKK #image #segmentation

Supervised Segmentation of Textures in Backscatter Images (PP, RPWD, GMPvK, RK), pp. 490–493.

ICPR-v3-2002-LicsarS #gesture #recognition

Supervised Training Based Hand Gesture Recognition System (AL, TS), pp. 999–1002.

ICPR-v3-2002-Saint-JeanF #algorithm #clustering #robust

A Robust Semi-Supervised EM-Based Clustering Algorithm with a Reject Option (CSJ, CF), pp. 399–402.

SIGIR-2002-AminiG #learning #summary

The use of unlabeled data to improve supervised learning for text summarization (MRA, PG), pp. 105–112.

SIGIR-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.

VLDB-2001-BaumgartnerFG01a #generative

Supervised Wrapper Generation with Lixto (RB, SF, GG), pp. 715–716.

KDD-2001-KaltonLWY #clustering #learning

Generalized clustering, supervised learning, and data assignment (AK, PL, KW, JPY), pp. 299–304.

KDD-2001-YamanishiT

Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner (KY, JiT), pp. 389–394.

SIGIR-2001-FranzMWZ #clustering #topic

Unsupervised and Supervised Clustering for Topic Tracking (MF, JSM, TW, WJZ), pp. 310–317.

CIKM-2000-HanK #algorithm #categorisation #documentation #performance #reduction #retrieval

Fast Supervised Dimensionality Reduction Algorithm with Applications to Document Categorization & Retrieval (EHH, GK), pp. 12–19.

CIKM-2000-KimL #clustering #documentation #information management

A Semi-Supervised Document Clustering Technique for Information Organization (HjK, SgL), pp. 30–37.

ICML-2000-GoldmanZ #learning

Enhancing Supervised Learning with Unlabeled Data (SAG, YZ), pp. 327–334.

ICML-2000-SchuurmansS #adaptation #learning

An Adaptive Regularization Criterion for Supervised Learning (DS, FS), pp. 847–854.

ICPR-v1-2000-MarlowO #generative #segmentation

Supervised Object Segmentation and Tracking for MPEG-4 VOP Generation (SM, NEO), pp. 5125–5128.

ICPR-v2-2000-NgB #classification #segmentation #using

Supervised Texture Segmentation using DWT and a Modified K-NN Classifier (BWN, AB), pp. 2545–2548.

ICPR-v2-2000-PerantonisPV #analysis #classification #component #paradigm #using

Supervised Principal Component Analysis Using a Smooth Classifier Paradigm (SJP, SP, VV), pp. 2109–2112.

ICPR-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.

ICDAR-1999-LebourgeoisBE #learning #using

Structure Relation between Classes for Supervised Learning using Pretopology (FL, MB, HE), pp. 33–36.

ICDAR-1999-PrevostM #online #recognition

Non-supervised Determination of Allograph Sub-classes for On-line Omni-scriptor Handwriting Recognition (LP, MM), pp. 438–441.

HCI-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.

KDD-1999-AggarwalGY #categorisation #clustering #on the

On the Merits of Building Categorization Systems by Supervised Clustering (CCA, SCG, PSY), pp. 352–356.

EDOC-1998-FerayBDT #object-oriented

An object-oriented software bus for supervision systems, based on DCOM (AF, FB, VD, FT), pp. 263–273.

ICPR-1998-FrelicotL #classification

A pretopology-based supervised pattern classifier (CF, FL), pp. 106–109.

ICPR-1998-Gimelfarb #interactive #modelling #question #segmentation #what

Supervised segmentation by pairwise interactions: do Gibbs models learn what we expect? (GLG), pp. 817–819.

HCI-SEC-1997-SantoniFF #adaptation #design #interface

Aid Methodology for Designing Adaptive Human Computer Interfaces for Supervision Systems (CS, EF, PF), pp. 501–504.

KDD-1997-ZighedRF #learning #multi

Optimal Multiple Intervals Discretization of Continuous Attributes for Supervised Learning (DAZ, RR, FF), pp. 295–298.

ICPR-1996-FrankH #approach #learning

Pretopological approach for supervised learning (FL, HE), pp. 256–260.

ICML-1995-DoughertyKS

Supervised and Unsupervised Discretization of Continuous Features (JD, RK, MS), pp. 194–202.

ICML-1994-LewisC #learning #nondeterminism

Heterogenous Uncertainty Sampling for Supervised Learning (DDL, JC), pp. 148–156.

ICML-1993-JordanJ #approach #divide and conquer #learning #statistics

Supervised Learning and Divide-and-Conquer: A Statistical Approach (MIJ, RAJ), pp. 159–166.

ML-1992-Janikow #contest #induction #learning

Combining Competition and Cooperation in Supervised Inductive Learning (CZJ), pp. 241–248.

ML-1991-JordanR #learning #modelling

Internal World Models and Supervised Learning (MIJ, DER), pp. 70–74.