Fuzzy support vector machines

A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that di...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2002-03, Vol.13 (2), p.464-471
Hauptverfasser:	Lin, Chun-Fu, Wang, Sheng-De
Format:	Artikel
Sprache:	eng
Schlagworte:	Fuzzy Fuzzy logic Fuzzy set theory Kernel Lagrangian functions Learning Machine learning Neural networks Noise reduction Quadratic programming Risk management Support vector machine classification Support vector machines
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Beschreibung
Zusammenfassung:	A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions to the learning of decision surface. We call the proposed method fuzzy SVMs (FSVMs).
ISSN:	1045-9227 2162-237X 1941-0093 2162-2388
DOI:	10.1109/72.991432