Title :
A multiclassification model based on FSVMs
Author :
Hu, B.Q. ; Yang, J. ; He, J.L.
Author_Institution :
Sch. of Math. & Stat., Wuhan Univ., China
Abstract :
Support vector machines (SVMs) proposed by Vapnik are the new method for small sample learning and are widely used in pattern classification and regression estimation. In multiclassfication there exist unclassifiable regions. In other words, some data are unclassifiable. This paper connects fuzzy membership with SVM to solve this problem, and gives a new classification model based on fuzzy support vector machines (FSVMs).
Keywords :
fuzzy set theory; learning (artificial intelligence); pattern classification; regression analysis; support vector machines; fuzzy membership; fuzzy support vector machine; multiclassification model; pattern classification; regression estimation; small sample learning; Helium; Kernel; Lagrangian functions; Machine learning; Pattern classification; Pattern recognition; Risk management; Statistics; Support vector machine classification; Support vector machines;
Conference_Titel :
Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American
Print_ISBN :
0-7803-9187-X
DOI :
10.1109/NAFIPS.2005.1548534
