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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

April 2007, Vol. 19, No. 4, Pages 1082-1096
(doi: 10.1162/neco.2007.19.4.1082)
© 2007 Massachusetts Institute of Technology
Recursive Finite Newton Algorithm for Support Vector Regression in the Primal
Article PDF (915.15 KB)
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.