Variable Selection for Support Vector Machines

References

• Asuncion , A. , Newman , D. J. ( 2007 ). UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html] . Irvine , CA : University of California, School of Information and Computer Science .
   Google Scholar
• Chapelle , O. , Vapnik , V. , Bousquet , O. , Mukherjee , S. ( 2002 ). Choosing multiple parameters for SVMs . Machine Learning 46 ( 1–3 ): 131 – 159 .
   Web of Science ®Google Scholar
• Chapelle , O. , Vapnik , V. , Bousquet , O. , Mukherjee , S. ( 2004 ). Choosing multiple parameters for support vector machines . Machine Learning 46 ( 1–3 ): 131 – 159 .
   Web of Science ®Google Scholar
• Claeskens , G. , Croux , C. , Van Kerkhoven , J. ( 2008 ). An information criterion for variable selection in support vector machines . Journal of Machine Learning Research 9 : 541 – 558 .
   Web of Science ®Google Scholar
• Couvreur , C. , Bresler , Y. ( 2000 ). On the optimality of the backward greedy algorithm for the subset selection problem . SIAM Journal on Matrix Analysis and Applications 21 ( 3 ): 797 – 808 .
   Web of Science ®Google Scholar
• Cristianini , N. , Kandola , J. , Elisseeff , A. , Shawe-Taylor , J. ( 2002 ). On kernel-target alignment . In: Advances in Neural Information Processing Systems , 14. Cambridge : MIT Press .
   Google Scholar
• Gestel , T. V. , Suykens , J. K. , Baesens , B. , Viaene , S. , Vanthienen , J. , Dedene , G. , Moor , B. D. , Vandewalle , J. ( 2004 ). Benchmarking least squares support vector machine classifiers . Machine Learning 54 : 5 – 32 .
   Web of Science ®Google Scholar
• Grandvalet , Y. , Canu , S. ( 2002 ). Adaptive scaling for feature selection in SVMs . Neural Information Processing Systems Paper AA09 .
   Google Scholar
• Guyon , I. , Elisseeff , A. ( 2003 ). An introduction to variable and feature selection . Journal of Machine Learning Research 3 : 1157 – 1182 .
   Google Scholar
• Keerthi , S. S. ( 2005 ). Generalized LARS as an effective feature selection tool for text classification with SVMs . ACM International Conference Proceeding Series 119 : 417 – 424 .
   Google Scholar
• Kohavi , J. , John , G. ( 1997 ). Wrappers for feature subset selection . Artificial Intelligence 97 : 273 – 324 .
   Web of Science ®Google Scholar
• Liu , H. , Li , J. , Wong , L. ( 2002 ). A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns . Genome Informatics 13 : 51 – 60 .
   PubMedGoogle Scholar
• Louw , N. , Steel , S. J. ( 2006 ). Variable selection in kernel Fisher discriminant analysis by means of recursive feature elimination . Computational Statistics and Data Analysis 51 ( 3 ): 2043 – 2055 .
   Web of Science ®Google Scholar
• Niijima , S. , Kuhara , S. ( 2006 ). Gene subset selection in kernel-induced feature space . Pattern Recognition Letters 27 ( 16 ): 1884 – 1892 .
   Web of Science ®Google Scholar
• Rakotomamonjy ( 2003 ). Variable selection using SVM based criteria . Journal of Machine Learning Research 3 : 1357 – 1370 .
   Google Scholar
• Schölkopf , B. , Smola , A. J. ( 2002 ). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond . London : MIT Press .
   Google Scholar
• Shawe-Taylor , J. , Cristianini , N. ( 2004 ). Kernel Methods for Pattern Analysis . Cambridge : Cambridge University Press .
   Google Scholar
• Stoppiglia , H. , Dreyfus , G. ( 2003 ). Ranking a random feature for variable and feature selection . Journal of Machine Learning Research 3 : 1399 – 1414 .
   Google Scholar
• Tibshirani , R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, B 58:267–288.
   Google Scholar
• Vapnik , V. , Chapelle , O. ( 2000 ). Bounds on error expectation for support vector machines . Neural Computation 12 ( 9 ): 2013 – 3036 .
   PubMed Web of Science ®Google Scholar
• Wang , W. , Xu , Z. , Lu , W. , Zhang , X. ( 2003 ). Determination of the spread parameter in the Gaussian kernel for classification and regression . Neurocomputing 55 : 643 – 663 .
   Web of Science ®Google Scholar
• Weston , J. , Mukherjee , S. , Chapelle , O. , Pontil , M. , Poggio , T. , Vapnik , V. ( 2001 ). Feature selection for SVMs . Advances in Neural Information Processing Systems 13 : 668 – 674 .
   Google Scholar
• Weston , J. , Elisseeff , A. , Schölkopf , B. , Tipping , M. ( 2003 ). Use of the zero norm with linear models and kernel methods . Journal of Machine Learning Research 3 : 1439 – 1461 .
   Google Scholar
• Zhang , H. H. ( 2006 ). Variable selection for support vector machines via smoothing spline ANOVA . Statistica Sinica 16 : 659 – 674 .
   Web of Science ®Google Scholar
• Zou , H. , Hastie , T. ( 2005 ). Regularization and variable selection via the elastic net . Journal of the Royal Statistical Society, B 67 : 301 – 320 .
   Google Scholar