A Practical and Robust Way to the Optimization of Parameters in RBF Kernel-Based One-Class Classification Support Vector Methods

Abstract

Supported by one-sided samples information alone, one-class classification problems are more difficult to deal with than those of the traditional two-class or multi-class classification in the sense of parameters optimization. Support vector data description (SVDD) has become one of the most popular kernel learning methods for solving one-class classification problems, while RBF kernel is the most widely used kernel function. Though a good many researchers have jointly employed SVDD and RBF kernel, a rare of them discussed the parameters optimization in detail. Pointing out the deficiencies of the existing concerned approaches, this research proposed a new and practical way to the optimization of parameters in RBF kernel-based SVDD. Experimental results of textural defects detection validate the proposed method.