Journal
PATTERN RECOGNITION LETTERS
Volume 25, Issue 14, Pages 1647-1656Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2004.06.009
Keywords
support vector machines; fuzzy membership; noise; optimization and classification
Categories
Ask authors/readers for more resources
The previous study of fuzzy support vector machines (FSVMs) provides a method to classify data with noises or outliers by manually associating each data point with a fuzzy membership that can reflect their relative degrees as meaningful data. In this paper, we introduce two factors in training data points, the confident factor and the trashy factor, and automatically generate fuzzy memberships of training data points from a heuristic strategy by using these two factors and a mapping function. We investigate and compare two strategies in the experiments and the results show that the generalization error of FSVMs is comparable to other methods on benchmark datasets. The proposed approach for automatic setting of fuzzy memberships makes the FSVMs more applicable in reducing the effects of noises or outliers. (C) 2004 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available