Recursive Finite Newton Algorithm for Support Vector Regression in the Primal

Authors:
Liefeng Bo;Ling Wang;Licheng Jiao
Affiliations:
blf0218@163.com;wliiip@163.com;Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China, lchjiao@mail.xidian.edu.cn
Venue:
Neural Computation
Year:
2007

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Abstract

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.