Neural Computation
April 2007, Vol. 19, No. 4, Pages 1082-1096
(doi: 10.1162/neco.2007.19.4.1082)
Recursive Finite Newton Algorithm for Support Vector Regression in the Primal
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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.