Title :
A Learning Method of Support Vector Machine Based on Particle Filters
Author :
Liangcheng, Cheng ; Huizhong, Yang
Author_Institution :
Sch. of Commun. & Control Eng., Jiangnan Univ., Wuxi, China
Abstract :
Support vector machine (SVM) is a novel and popular technique for pattern classification and regression estimation. In the training process of SVM it is of great importance to determine a few tuning parameters to ensure the good performance of SVM. However, the widely used optimization methods such as Particle Swarm Optimization and Genetic Algorithm have the disadvantages of low convergent speed and limited overall searching ability. To solve this problem, this paper proposes an alternative approach whereby particle filters are used to estimate the key parameters in the training process of SVM. The SVM model built in this way is used to estimate process variables in the production of Bisphenol A. Simulations show the effectiveness of this method.
Keywords :
genetic algorithms; particle filtering (numerical methods); particle swarm optimisation; pattern classification; support vector machines; Bisphenol A; genetic algorithm; overall searching ability; particle filters; particle swarm optimization; pattern classification; regression estimation; support vector machine; tuning parameters; Genetic algorithms; Learning systems; Optimization methods; Parameter estimation; Particle filters; Particle swarm optimization; Pattern classification; Production; Support vector machine classification; Support vector machines; parameter estimation; particle filters; state space component; support vector machine;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.81