Recursive finite Newton algorithm for support vector regression in the primal. (English) Zbl 1118.68114

Summary: 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.


68T05 Learning and adaptive systems in artificial intelligence
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62M45 Neural nets and related approaches to inference from stochastic processes


Full Text: DOI


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