Parameters Selection of SVM Based on Genetic Algorithm
Tian Xue · 2004
Support Vector Machines (SVM) is a promising learning technique. While in practice, the problem on how to select parameters of SVM is not solved properly. In order to get the optimal parameters automatically, a new approach based on genetic algorithm was proposed, which can acquire the best parameters of SVM. This method defines the search area by analyzing the behavior of SVMs with different parameters that also have different influence on the classrate and then chooses the best parameters in the given region. The method is experimented with five benchmark repotsch, the results demonstrate that the algorithm can get the SVM with the best recognition accuracy and simple structure.