Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
388436 | Expert Systems with Applications | 2013 | 6 Pages |
Abstract
Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters, and finds critical data in clusters as a substitute for the original data to reduce the computational complexity. The computational experiments presented in this paper show that the clustering algorithm significantly advances SVM learning efficiency.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Der-Chiang Li, Yao-Hwei Fang,