Title :
Fuzzy support vector machines
Author :
Lin, Chun-Fu ; Wang, Sheng-De
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taiwan
fDate :
3/1/2002 12:00:00 AM
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)
Keywords :
fuzzy set theory; learning automata; pattern classification; classification; fuzzy membership; quadratic programming; support vector machine; Helium; Kernel; Lagrangian functions; Machine learning; Noise reduction; Quadratic programming; Risk management; Support vector machine classification; Support vector machines;
Journal_Title :
Neural Networks, IEEE Transactions on
