Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22120
Title: A hyper-sphere SVM introduced the margin
Authors: Zhang, XF
Zhuo, L
Feng, D
Keywords: Generalization performance
Margin
Hyper-sphere SVM
Issue Date: 2008
Publisher: IEEE
Source: 2008 International Conference on Neural Networks and Signal Processing, 7-11 June 2008, Nanjing, p. 470-475 How to cite?
Abstract: Binary hyper-sphere support vector machine (SVM) is a new method for data description. Its weakness is that the margin between two classes of samples is zero or an uncertain value, which affects the classifier's generalization performance to some extent. So a generalized hyper-sphere SVM (GHSSVM) is provided in this paper. By introducing the parameter n and b (n>b), the margin which is greater than zero may be obtained. The experimental results show the proposed classifier may have better generalization performance and the less experimental risk than the hyper-sphere SVM in the references.
URI: http://hdl.handle.net/10397/22120
ISBN: 978-1-4244-2310-1
978-1-4244-2311-8 (E-ISBN)
DOI: 10.1109/ICNNSP.2008.4590395
Appears in Collections:Conference Paper

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