Fuzzy support vector machines

Authors:
Chun-Fu Lin;Sheng-De Wang
Affiliations:
Dept. of Electr. Eng., Nat. Taiwan Univ.;-
Venue:
IEEE Transactions on Neural Networks
Year:
2002

Citing 0
Cited 105

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Abstract

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)