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SVDD-Based Method for Fast Training of Multi-class Support Vector ClassifierKANG, Woo-Sung; KI HONG IM; JIN YOUNG CHOI et al.Lecture notes in computer science. 2006, pp 991-996, issn 0302-9743, isbn 3-540-34439-X, 6 p.Conference Paper

The Novelty Detection Approach for Different Degrees of Class ImbalanceLEE, Hyoung-Joo; CHO, Sungzoon.Lecture notes in computer science. 2006, pp 21-30, issn 0302-9743, isbn 3-540-46479-4, 3Vol, 10 p.Conference Paper

Artificial aging of faces by Support Vector MachinesJIANNING WANG; LING, Charles X.Lecture notes in computer science. 2004, pp 499-503, issn 0302-9743, isbn 3-540-22004-6, 5 p.Conference Paper

Maximum-margin classification of sequential data with infinitely-long temporal dependencies [Exp. Syst. Appl. 40 (11) (2013) 4519―4527]: CorrigendumCHATZIS, Sotirios P.Expert systems with applications. 2013, Vol 40, Num 17, issn 0957-4174, p. 6713Article

A comparison of model selection methods for multi-class support vector machinesHUAQING LI; FEIHU QI; SHAOYU WANG et al.Lecture notes in computer science. 2005, issn 0302-9743, isbn 3-540-25860-4, 4Vol, part IV, 1140-1148Conference Paper

Learning SVM kernel with semi-definite programmingSHUZHONG YANG; SIWEI LUO.Lecture notes in computer science. 2005, issn 0302-9743, isbn 3-540-28323-4, 3Vol, Part 1, 710-715Conference Paper

Feature selection for fast image classification with support vector machinesFAN, Zhi-Gang; WANG, Kai-An; LU, Bao-Liang et al.Lecture notes in computer science. 2004, pp 1026-1031, issn 0302-9743, isbn 3-540-23931-6, 6 p.Conference Paper

Protein structural class determination using Support Vector MachinesISIK, Zerrin; YANIKOGLU, Berrin; SEZERMAN, Ugur et al.Lecture notes in computer science. 2004, pp 82-89, issn 0302-9743, isbn 3-540-23526-4, 8 p.Conference Paper

Constant rate approximate maximum margin algorithmsTSAMPOUKA, Petroula; SHAWE-TAYLOR, John.Lecture notes in computer science. 2006, pp 437-448, issn 0302-9743, isbn 3-540-45375-X, 1Vol, 12 p.Conference Paper

Convergence of a new decomposition algorithm for support vector machinesWANG, Yan-Guo; HONG QIAO; BO ZHANG et al.Lecture notes in computer science. 2006, pp 303-308, issn 0302-9743, isbn 3-540-37274-1, 6 p.Conference Paper

Proximal SVM Ensemble Based on Feature SelectionXIAOYAN TAO; HONGBING JI; ZHIQIANG MA et al.Lecture notes in computer science. 2006, issn 0302-9743, isbn 3-540-45901-4, Vol1,135-138Conference Paper

Rotational prior knowledge for SVMsEPSHTEYN, Arkady; DEJONG, Gerald.Lecture notes in computer science. 2005, pp 108-119, issn 0302-9743, isbn 3-540-29243-8, 1Vol, 12 p.Conference Paper

Clifford Support Vector machines for classificationBAYRO-CORROCHANO, Eduardo; ARANA-DANIEL, Nancy; VALLEJO-GUTIERRES, J. Refugio et al.Lecture notes in computer science. 2004, pp 9-16, issn 0302-9743, isbn 3-540-22123-9, 8 p.Conference Paper

Twin support vector machine with Universum dataZHIQUAN QI; YINGJIE TIAN; YONG SHI et al.Neural networks. 2012, Vol 36, pp 112-119, issn 0893-6080, 8 p.Article

Forecasting S&P500 Index Movement with Support Vector MachinesROSILLO, R; DE LA FUENTE, D; BRUGOS, J. A. L et al.Artificial intelligence. International conferenceWorldComp'2011. 2011, pp 648-653, isbn 1-60132-183-X 1-60132-184-8 1-60132-185-6, 6 p.Conference Paper

A Comparison of Tools for Detecting Fake WebsitesABBASI, Ahmed; CHEN, Hsinchun.Computer (Long Beach, CA). 2009, Vol 42, Num 10, pp 78-86, issn 0018-9162, 9 p.Article

Embracing and extending 20th-century instruction set architecturesGEBIS, Joe; PATTERSON, David.Computer (Long Beach, CA). 2007, Vol 40, Num 4, pp 68-75, issn 0018-9162, 8 p.Article

A Novel Method for Rapid Speaker Adaptation Using Reference Support Speaker SelectionJIAN WANG; ZHEN YANG; JIANJUN LEI et al.Lecture notes in computer science. 2006, pp 450-456, issn 0302-9743, isbn 3-540-49667-X, 1Vol, 7 p.Conference Paper

A new alpha seeding method for support vector machine trainingDU FENG; WENKANG SHI; HUAWEI GUO et al.Lecture notes in computer science. 2005, issn 0302-9743, isbn 3-540-28323-4, 3Vol, Part 1, 679-682Conference Paper

Increasing the efficiency of support vector machine by simplifying the shape of separation hypersurfaceYIQIANG ZHAN; DINGGANG SHEN.Lecture notes in computer science. 2004, pp 732-738, issn 0302-9743, isbn 3-540-24127-2, 7 p.Conference Paper

Adaptive feature selection via a new version of support vector machineJUNYAN TAN; ZHIQIANG ZHANG; LING ZHEN et al.Neural computing & applications (Print). 2013, Vol 23, Num 3-4, pp 937-945, issn 0941-0643, 9 p.Article

Geometric algorithms for parametric-margin v-support vector machineXINJUN PENG; DONG XU.Neurocomputing (Amsterdam). 2013, Vol 99, pp 197-205, issn 0925-2312, 9 p.Article

An extension to fuzzy support vector data description (FSVDD)FORGHANI, Y; SADOGHI YAZDI, H; EFFATI, S et al.Pattern analysis and applications (Print). 2012, Vol 15, Num 3, pp 237-247, issn 1433-7541, 11 p.Article

Structural Regularized Support Vector Machine: A Framework for Structural Large Margin ClassifierHUI XUE; SONGCAN CHEN; QIANG YANG et al.IEEE transactions on neural networks. 2011, Vol 22, Num 4, pp 573-587, issn 1045-9227, 15 p.Article

A new local-global approach for classificationPERES, R. T; PEDREIRA, C. E.Neural networks. 2010, Vol 23, Num 7, pp 887-891, issn 0893-6080, 5 p.Article

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