Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
383862 | Expert Systems with Applications | 2010 | 11 Pages |
Abstract
One of the significant research problems in support vector machines (SVM) is the selection of optimal parameters that can establish an efficient SVM so as to attain desired output with an acceptable level of accuracy. The present study adopts ant colony optimization (ACO) algorithm to develop a novel ACO-SVM model to solve this problem. The proposed algorithm is applied on some real world benchmark datasets to validate the feasibility and efficiency, which shows that the new ACO-SVM model can yield promising results.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
XiaoLi Zhang, XueFeng Chen, ZhengJia He,