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(Neural Computation. 2007;19:1082-1096.)
© 2007 The MIT Press

Letter

Recursive Finite Newton Algorithm for Support Vector Regression in the Primal

Liefeng Bo

blf0218{at}163.com

Ling Wang

wliiip{at}163.com

Licheng Jiao

lchjiao{at}mail.xidian.edu.cn Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China

Some algorithms in the primal have been recently proposed for training support vector machines. This letter follows those studies and develops a recursive finite Newton algorithm (IHLF-SVR-RFN) for training nonlinear support vector regression. The insensitive Huber loss function and the computation of the Newton step are discussed in detail. Comparisons with LIBSVM 2.82 show that the proposed algorithm gives promising results.






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J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 2007 by The MIT Press.