Stem svms$ (all stems)

38 papers:

CIKM-2014-MaoWHO #classification #learning #linear #multi

Nonlinear Classification via Linear SVMs and Multi-Task Learning (XM, OW, WH, PO), pp. 1955–1958.

CIKM-2014-SchneiderBV #linear #multi

Solving Linear SVMs with Multiple 1D Projections (JS, JB, MV), pp. 221–230.

ICPR-2014-ChernousovaLTMW #parametricity #validation

Non-enumerative Cross Validation for the Determination of Structural Parameters in Feature-Selective SVMs (EC, PL, AT, VM, DW), pp. 3654–3659.

ICML-c1-2013-Lacoste-JulienJSP #coordination #optimisation

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs (SLJ, MJ, MWS, PP), pp. 53–61.

ICML-c3-2013-TakacBRS

Mini-Batch Primal and Dual Methods for SVMs (MT, ASB, PR, NS), pp. 1022–1030.

ICML-2012-MalisiewiczSGE #detection #image #retrieval #visual notation

Exemplar-SVMs for Visual Ob ject Detection, Label Transfer and Image Retrieval (TM, AS, AG, AAE), p. 4.

CIAA-J-2010-AllauzenCM11 #algorithm #coordination #kernel

A Dual Coordinate Descent Algorithm for SVMs Combined with Rational Kernels (CA, CC, MM), pp. 1761–1779.

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.

KDD-2011-CotterSK #approach #kernel

A GPU-tailored approach for training kernelized SVMs (AC, NS, JK), pp. 805–813.

CIAA-2010-AllauzenCM #automaton #kernel #scalability

Large-Scale Training of SVMs with Automata Kernels (CA, CC, MM), pp. 17–27.

ICML-2010-Masnadi-ShiraziV #elicitation #probability

Risk minimization, probability elicitation, and cost-sensitive SVMs (HMS, NV), pp. 759–766.

ICML-2010-SonnenburgF #framework #linear #named

COFFIN: A Computational Framework for Linear SVMs (SS, VF), pp. 999–1006.

ICML-2009-YuJ #learning

Learning structural SVMs with latent variables (CNJY, TJ), pp. 1169–1176.

ICML-2008-FinleyJ

Training structural SVMs when exact inference is intractable (TF, TJ), pp. 304–311.

ICML-2008-YueJ #predict #set #using

Predicting diverse subsets using structural SVMs (YY, TJ), pp. 1224–1231.

ICPR-2008-YenCWWC #using #video

A scene-based video watermarking technique using SVMs (SHY, HWC, CJW, PSPW, MCC), pp. 1–4.

KDD-2008-KeerthiSCHL #linear #multi #scalability

A sequential dual method for large scale multi-class linear svms (SSK, SS, KWC, CJH, CJL), pp. 408–416.

KDD-2008-YuJ #kernel #using

Training structural svms with kernels using sampled cuts (CNJY, TJ), pp. 794–802.

SIGIR-2007-SculleyW #online

Relaxed online SVMs for spam filtering (DS, GW), pp. 415–422.

ICML-2006-ChapelleCZ #continuation

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

ICML-2006-PandaCW #bound #concept #detection

Concept boundary detection for speeding up SVMs (NP, EYC, GW), pp. 681–688.

ICML-2006-ShivaswamyJ #invariant #permutation

Permutation invariant SVMs (PKS, TJ), pp. 817–824.

ICPR-v2-2006-Huang #kernel #predict

A New Kernel Based on Weighted Cross-Correlation Coefficient for SVMs and Its Application on Prediction of T-cell Epitopes (JH), pp. 691–694.

ICPR-v3-2006-ArreolaFB #classification #linear #performance #using

Fast Support Vector Machine Classification using linear SVMs (KZA, JF, HB), pp. 366–369.

ICPR-v3-2006-LiGYCG #feature model

Facial Feature Selection Based on SVMs by Regularized Risk Minimization (WL, WG, LY, WC, XG), pp. 540–543.

KDD-2006-Joachims #linear

Training linear SVMs in linear time (TJ), pp. 217–226.

SIGIR-2006-SindhwaniK #linear #scalability

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

ICML-2005-Keerthi #classification #effectiveness #feature model

Generalized LARS as an effective feature selection tool for text classification with SVMs (SSK), pp. 417–424.

ICML-2004-CollobertB

Links between perceptrons, MLPs and SVMs (RC, SB).

ICML-2004-GabrilovichM #categorisation #feature model #using

Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5 (EG, SM).

ICML-2004-Jebara #kernel #multi

Multi-task feature and kernel selection for SVMs (TJ).

ICPR-v2-2004-IurgelR #classification #documentation #probability #using

Spoken Document Classification with SVMs Using Linguistic Unit Weighting and Probabilistic Couplers (UI, GR), pp. 667–670.

ICPR-v3-2004-PozdnoukhovB #classification #image #invariant #kernel

Tangent Vector Kernels for Invariant Image Classification with SVMs (AP, SB), pp. 486–489.

ICPR-v3-2004-YingK #evaluation #performance

Fast Leave-One-Out Evaluation and Improvement on Inference for LS-SVMs (ZY, KCK), pp. 494–497.

ICPR-v4-2004-WangJ #detection #multi

Multi-View Face Detection under Complex Scene based on Combined SVMs (PW, QJ), pp. 179–182.

DRR-2003-RahmanTA #heuristic #hybrid #web

Exploring a hybrid of support vector machines (SVMs) and a heuristic-based system in classifying web pages (AFRR, YT, HA), pp. 120–127.

ICML-2003-Bi #multi #programming

Multi-Objective Programming in SVMs (JB), pp. 35–42.

KDD-2003-YuYH #clustering #scalability #set #using

Classifying large data sets using SVMs with hierarchical clusters (HY, JY, JH), pp. 306–315.