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Title: Separating two classes of samples using support vectors in a convex hull
Authors: Wu, C
Yeung, DS
Tsang, ECC
Keywords: Hilbert spaces
Computational geometry
Pattern classification
Set theory
Support vector machines
Issue Date: 2005
Publisher: IEEE
Source: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005, 18-21 August 2005, Guangzhou, China, v. 7, p. 4233-4236 How to cite?
Abstract: In this paper, we present the necessary and sufficient conditions of two finite classes of samples that can be separated by a hyperplane in terms of support vectors which are just the vertices of a convex hull of each class of samples. We also extend the calculating formula of the margin of an optimal separating hyperplane to some cases of the classes of infinite samples in Hilbert space. These results are the generalization and improvement of the corresponding results for the theory of SVM in Euclidian space.
ISBN: 0-7803-9091-1
DOI: 10.1109/ICMLC.2005.1527680
Appears in Collections:Conference Paper

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