A practical and robust way to the optimization of parameters in RBF Kernel-based one-class classification support vector methods

Authors:
Hong-Gang Bu;Jun Wang;Xiu-Bao Huang
Affiliations:
College of Textiles, Donghua University Shanghai, China;College of Textiles, Donghua University Shanghai, China and Key Laboratory of Textile Science & Technology of Ministry of Education, Shanghai, China;College of Textiles, Donghua University Shanghai, China
Venue:
ICNC'09 Proceedings of the 5th international conference on Natural computation
Year:
2009

Citing 4
Cited 0

Uniform object generation for optimizing one-class classifiers

The Journal of Machine Learning Research
Support Vector Data Description

Machine Learning
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

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.