Support Vector Machines with Embedded Reject Option (0)

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by Giorgio Fumera , Fabio Roli
Venue:Proceedings of the Int. Workshop on Pattern Recognition with Support Vector Machines (SVM2002), Niagara Falls
Citations:9 - 1 self

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6 Cost-sensitive learning in Support Vector Machines – Giorgio Fumera, Fabio Roli - 2002
74 Support vector machines for speech recognition – Aravind Ganapathiraju, Jonathan Hamaker, Joseph Picone - 1998
28 New results on error correcting output codes of kernel machines – Andrea Passerini, Massimiliano Pontil, Paolo Frasconi - 2004
ACKNOWLEDGEMENTS – Et Des Mathematiques, Spécialité Automatique, Abdul Rahim Ahmad, Ogier Professeur, Universite De La Rochelle, Chee Peng Lim, Universiti Sains Malaysia, Examinateurs Sheikh, Hussain Shaikh, Salleh Professeur, Universiti Teknologi Malaysia, Patrick Le Callet, Universite De Nantes, Marzuki Khalid, Christian Viard-gaudi N, Universite De Nantes - 2009
473 A tutorial on support vector regression – Alex J. Smola, Bernhard Schölkopf - 2004
LETTER Communicated by John Platt Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis – T. Van Gestel, J. A. K. Suykens, G. Lanckriet, A. Lambrechts, B. De Moor, J. Vandewalle
19 Bayesian framework for least squares support vector machine classifiers, Gaussian processes and kernel fisher discriminant analysis – Tony Van Gestel, Johan A. K. Suykens, Gert Lanckriet, Annemie Lambrechts, Bart De Moor, Joos Vandewalle - 2002
15 The Interplay of Optimization and Machine Learning Research – Kristin P. Bennett, P. Bennett, Emilio Parrado-Hernandez - 2006
13 A tutorial on ν-Support Vector Machines – Pai-hsuen Chen, Chih-jen Lin, Bernhard Schölkopf - 2005
1 An Introductory Example – Pai-hsuen Chen, Chih-jen Lin, Bernhard Schölkopf
108 The analysis of decomposition methods for support vector machines – Chih-jen Lin, Nello Cristianini - 1999
88 Everything Old Is New Again: A Fresh Look at Historical Approaches – Ryan Michael Rifkin - 2002
9 Optimization of the SVM Kernels using an Empirical Error Minimization Scheme. – N.E. Ayat, M. Cheriet, C. Y. Suen - 2002
3412 LIBSVM: a Library for Support Vector Machines – Chih-chung Chang, Chih-Jen Lin - 2001
23 T.: Secondary structure prediction with support vector machines – J. J. Ward, L. J. Mcguffin, B. F. Buxton, D. T. Jones
Sparse Nonlinear Discriminants – Edin Andelic - 2007
27 Combining protein secondary structure prediction models with ensemble methods of optimal complexity – Yann Guermeur , Gianluca Pollastri , Andre Elisseeff, Dominique Zelus ,Helene Paugam-Moisy , Pierre Baldi - 2004
373 An introduction to kernel-based learning algorithms – Klaus-Robert Müller, Sebastian Mika, Gunnar Rätsch, Koji Tsuda, Bernhard Schölkopf - 2001
32 Comparison of support vector machine and artificial neural network systems for drug/ nNondrug classification – Evgeny Byvatov, Uli Fechner, Jens Sadowski, Gisbert Schneider