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Fuzzy support vector machines

IEEE Transactions on Neural Networks · 2002 · Vol. 13(2) · pp. 464–471

Chun-Fu Lin✉(National Taiwan University)Sheng‐De Wang(National Taiwan University)

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).

Face and Expression Recognition Neural Networks and Applications Image Retrieval and Classification TechniquesSupport vector machineComputer scienceArtificial intelligenceFuzzy logicPoint (geometry)Machine learningFuzzy setRelevance vector machineStructured support vector machineFuzzy classification

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