DocumentCode
1263945
Title
Fuzzy support vector machines
Author
Lin, Chun-Fu ; Wang, Sheng-De
Author_Institution
Dept. of Electr. Eng., Nat. Taiwan Univ., Taiwan
Volume
13
Issue
2
fYear
2002
fDate
3/1/2002 12:00:00 AM
Firstpage
464
Lastpage
471
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;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.991432
Filename
991432
Link To Document