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.