Tuning of the hyperparameters for L2-loss SVMs with the RBF kernel by the maximum-margin principle and the jackknife technique. Issue 12 (December 2015)

• Record Type: Journal Article
• Authors: Chang, Chin-Chun; Chou, Shen-Huan
• Is Part Of: Pattern recognition. Volume 48:Issue 12(2015:Dec.)
• Page Start: 3983
• Page End: 3992
• Publication Date: 2015-12
• Subjects: Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.06.017
• Languages: English
• ISSNs: 0031-3203

Abstract

<abstract abstract-type="author" id="ab0005"> <title id="sect0005">Abstract</title> <sec> <p id="sp0045">The hyperparameters for support vector machines (SVMs) with <italic>L</italic>2 soft margins and the radial basis function (RBF) kernel include the parameters for the RBF kernel and the <italic>L</italic>2-soft-margin parameter <italic>C</italic>. In this paper, the parameters for the RBF kernel are determined through maximization of a margin-based criterion. This criterion is approximately optimized through solving two easier subproblems: one is related to margin maximization in the input space and the other is related to the determination of the extent of sample spread in the feature space. After that, the <italic>L</italic>2-soft-margin parameter <italic>C</italic> is obtained by an analytic formula in terms of a jackknife estimate of the perturbation in the eigenvalues of the kernel matrix. In comparison with SVM model selection based on differentiable bounds, such as radius/margin bounds, experimental results on a number of open data sets show that the proposed approach is efficient and accurate.</p> </sec> </abstract>