Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
532883 | Pattern Recognition | 2007 | 11 Pages |
Abstract
In this paper, a novel classifier for multi-classification problems is proposed. The proposed classifier, based on the Bayesian optimal decision theory, tries to model the decision boundaries via the posterior probability distributions constructed from support vector domain description rather than to model them via the optimal hyperplanes constructed from two-class support vector machines. Experimental results show that the proposed method is more accurate and efficient for multi-classification problems.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Daewon Lee, Jaewook Lee,