Stem multiclass$ (all stems)
55 papers:
CASE-2015-BaeM #markov #modelling #multi #random- Markovian modeling of multiclass deterministic flow lines with random arrivals: The case of a single-channel (SYB, JRM), pp. 649–654.
ICML-2015-NarasimhanRS0 #algorithm #consistency #metric #multi #performance- Consistent Multiclass Algorithms for Complex Performance Measures (HN, HGR, AS, SA), pp. 2398–2407.
ICML-c1-2014-ChenLL #multi #online #problem- Boosting with Online Binary Learners for the Multiclass Bandit Problem (STC, HTL, CJL), pp. 342–350.
ICML-c2-2014-BeijbomSKV #multi- Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting (OB, MJS, DJK, NV), pp. 586–594.
ICML-c2-2014-KontorovichW #multi #nearest neighbour- Maximum Margin Multiclass Nearest Neighbors (AK, RW), pp. 892–900.
ICPR-2014-BlondelFU #multi #scalability- Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex (MB, AF, NU), pp. 1289–1294.
CIKM-2013-ChengCLWAC #data type #learning #multi- Feedback-driven multiclass active learning for data streams (YC, ZC, LL, JW, AA, ANC), pp. 1311–1320.
ICML-c3-2013-Agarwal #algorithm #multi #predict- Selective sampling algorithms for cost-sensitive multiclass prediction (AA), pp. 1220–1228.
ICML-c3-2013-LongS #classification #consistency #multi- Consistency versus Realizable H-Consistency for Multiclass Classification (PML, RAS), pp. 801–809.
ICML-c3-2013-PiresSG #bound #classification #multi- Cost-sensitive Multiclass Classification Risk Bounds (BAP, CS, MG), pp. 1391–1399.
MLDM-2013-EichelbergerS #classification #empirical #multi- An Empirical Study of Reducing Multiclass Classification Methodologies (RKE, VSS), pp. 505–519.
ICML-2012-ReidWS #design #multi- The Convexity and Design of Composite Multiclass Losses (MDR, RCW, PS), p. 36.
ICPR-2012-JiSL #multi- Multitask multiclass privileged information support vector machines (YJ, SS, YL), pp. 2323–2326.
ICPR-2012-LiYLKZL #classification #multi #using- Multiclass boosting SVM using different texture features in HEp-2 cell staining pattern classification (KL, JY, ZL, XK, RZ, WL), pp. 170–173.
ICML-2011-CrammerG #adaptation #classification #feedback #multi #using- Multiclass Classification with Bandit Feedback using Adaptive Regularization (KC, CG), pp. 273–280.
ICML-2011-GaoK #multi- Multiclass Boosting with Hinge Loss based on Output Coding (TG, DK), pp. 569–576.
KDD-2011-ValizadeganJW #learning #multi #predict- Learning to trade off between exploration and exploitation in multiclass bandit prediction (HV, RJ, SW), pp. 204–212.
ICML-2010-TuL #classification #multi- One-sided Support Vector Regression for Multiclass Cost-sensitive Classification (HHT, HTL), pp. 1095–1102.
ICPR-2010-HuangDF #classification #estimation #multi #random- Head Pose Estimation Based on Random Forests for Multiclass Classification (CH, XD, CF), pp. 934–937.
ICPR-2010-LeeWC #classification #linear #multi- A Discriminative and Heteroscedastic Linear Feature Transformation for Multiclass Classification (HSL, HMW, BC), pp. 690–693.
ICPR-2010-LuoN #classification #fault #learning #multi #problem- Employing Decoding of Specific Error Correcting Codes as a New Classification Criterion in Multiclass Learning Problems (YL, KN), pp. 4238–4241.
ICPR-2010-NikitidisNP #incremental #multi- Incremental Training of Multiclass Support Vector Machines (SN, NN, IP), pp. 4267–4270.
ICPR-2010-VerschaeR #detection #multi- Coarse-To-Fine Multiclass Nested Cascades for Object Detection (RV, JRdS), pp. 344–347.
SIGIR-2009-AmbaiY #clustering #image #multi #ranking #set #visual notation- Multiclass VisualRank: image ranking method in clustered subsets based on visual features (MA, YY), pp. 732–733.
ICML-2008-KakadeST #algorithm #multi #online #performance #predict- Efficient bandit algorithms for online multiclass prediction (SMK, SSS, AT), pp. 440–447.
ICML-2008-ZhaoWZ #clustering #multi #performance- Efficient multiclass maximum margin clustering (BZ, FW, CZ), pp. 1248–1255.
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-LiZWH #analysis #clustering #multi- Multiclass spectral clustering based on discriminant analysis (XL, ZZ, YW, WH), pp. 1–4.
ICML-2007-AmitFSU #classification #multi- Uncovering shared structures in multiclass classification (YA, MF, NS, SU), pp. 17–24.
ICML-2007-AsharafMS #multi- Multiclass core vector machine (SA, MNM, SKS), pp. 41–48.
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-BordesBGW #multi- Solving multiclass support vector machines with LaRank (AB, LB, PG, JW), pp. 89–96.
ICML-2007-ZienO #kernel #learning #multi- Multiclass multiple kernel learning (AZ, CSO), pp. 1191–1198.
MLDM-2007-HulsmannF #algorithm #comparison #multi #novel #optimisation #parametricity- Comparison of a Novel Combined ECOC Strategy with Different Multiclass Algorithms Together with Parameter Optimization Methods (MH, CMF), pp. 17–31.
SAC-2007-LimaSC #estimation #metric #multi #network- Enhancing QoS metrics estimation in multiclass networks (SRL, PNMdS, PC), pp. 227–231.
ICML-2006-FinkSSU #learning #multi #online- Online multiclass learning by interclass hypothesis sharing (MF, SSS, YS, SU), pp. 313–320.
ICML-2006-GeJ #approximate #consistency #multi- A note on mixtures of experts for multiclass responses: approximation rate and Consistent Bayesian Inference (YG, WJ), pp. 329–335.
ICML-2006-Li #clustering #multi- Multiclass boosting with repartitioning (LL), pp. 569–576.
ICML-2006-TangM #multi- Multiclass reduced-set support vector machines (BT, DM), pp. 921–928.
ICPR-v3-2006-KierA #classification #multi #predict- Predicting the benefit of sample size extension in multiclass k-NN classification (CK, TA), pp. 332–335.
ICPR-v4-2006-LefaucheurN #classification #multi #robust #symmetry- Robust Multiclass Ensemble Classifiers via Symmetric Functions (PL, RN), pp. 136–139.
ICML-2004-GaoWLC #approach #categorisation #learning #multi #robust- A MFoM learning approach to robust multiclass multi-label text categorization (SG, WW, CHL, TSC).
ICPR-v2-2004-XuanDKHCW #feature model #multi #predict #profiling #robust- Robust Feature Selection by Weighted Fisher Criterion for Multiclass Prediction in Gene Expression Profiling (JX, YD, JIK, EPH, RC, YJW), pp. 291–294.
ICPR-v3-2004-KoKB04a #learning #multi #problem- Improved N-Division Output Coding for Multiclass Learning Problems (JK, EK, HB), pp. 470–473.
ICPR-v3-2004-SotocaSP #multi #naive bayes #set #using- Attribute Relevance in Multiclass Data Sets Using the Naive Bayes Rule (JMS, JSS, FP), pp. 426–429.
ICPR-v4-2004-OuMF #classification #multi #network #using- Multiclass Pattern Classification Using Neural Networks (GO, YLM, LAF), pp. 585–588.
ICDAR-2003-HamamuraMI #classification #multi- A Multiclass Classification Method Based on Multiple Pairwise Classifiers (TH, HM, BI), pp. 809–813.
SAC-2003-GiorgettiS #automation #bibliography #categorisation #multi- Multiclass Text Categorization for Automated Survey Coding (DG, FS), pp. 798–802.
ICML-2002-SlonimBFT #feature model #markov #memory management #multi- Discriminative Feature Selection via Multiclass Variable Memory Markov Model (NS, GB, SF, NT), pp. 578–585.
ICPR-v2-2002-TaxD #classification #multi #using- Using Two-Class Classifiers for Multiclass Classification (DMJT, RPWD), pp. 124–127.
KDD-2002-ZadroznyE #classification #multi #probability- Transforming classifier scores into accurate multiclass probability estimates (BZ, CE), pp. 694–699.
ICML-2000-AllweinSS #approach #classification #multi- Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers (ELA, RES, YS), pp. 9–16.
ICPR-v2-2000-LeeC #feature model #multi #optimisation #problem- Optimizing Feature Extraction for Multiclass Problems (CL, EC), pp. 2402–2405.
ICML-1997-Schapire #learning #multi #problem #using- Using output codes to boost multiclass learning problems (RES), pp. 313–321.
VLDB-1993-BrownCL #memory management #multi- Managing Memory to Meet Multiclass Workload Response Time Goals (KPB, MJC, ML), pp. 328–341.