Parameter Optimization and Application of Support Vector Machine Based on Parallel Artificial Fish Swarm Algorithm

Abstract

Parameters selection of support vector machine is a very important problem, which has great influence on its performance. In order to improve the learning and generalization ability of support vector machine, in this paper, proposed a new algorithm -parallel artificial fish swarm algorithm to optimize kernel parameter and penalty factor of support vector machine, improved the loop body of artificial fish swarm algorithm to avoid the missing of the optimum solution, and proved its validity by testing with some test functions; used the optimal parameters in a non-specific persons, isolated words, and medium-vocabulary speech recognition system. The experimental results show that the rates of speech recognition based on support vector machine using the new algorithm are better than those of using the traditional artificial fish swarm algorithm in different signal to noise ratios and different words. Especially, the support vector machine model based on the new algorithm can still maintain better recognition rates in lower signal to noise ratios. So the new algorithm is an effective support vector machine parameter optimization method, which makes the support vector machine not only have good generalization ability, but have better robustness.