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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 1/2011

01-03-2011 | Original Article

Separating theorem of samples in Banach space for support vector machine learning

Authors: Qiang He, Congxin Wu

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2011

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Abstract

The theory of machine learning in Banach space is a new research topic and has drawn much attention in recent years. The theoretical foundation of this topic is that under what conditions two sample sets can be separated in Banach space. In this paper, motivated by developing new support vector machine (SVM) in Banach space, we present a necessary and sufficient condition of separating two finite classes of samples by a hyper-plane in Banach space. We also present an attainable expression of maximal margin of the separating hyper-planes which includes some cases of the classes of infinite samples in Banach space.
previous article Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery

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Metadata
Title
Separating theorem of samples in Banach space for support vector machine learning
Authors
Qiang He
Congxin Wu
Publication date
01-03-2011
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2011
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-011-0013-4

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