Probability Model of Covering Algorithm (PMCA)

Shu Zhao, Yanping Zhang, Ling Zhang, Ping Zhang, Ying-chun Zhang

Abstract: The probability model is introduced into classification learning in this paper. Kernel covering algorithm (KCA) and maximum likelihood principle of the statistic model combine to form a novel algorithm-the probability model of covering algorithm (PMCA) which not only introduces optimization processing for every covering domain, but offers a new way to solve the parameter problem of kernel function. Covering algorithm (CA) is firstly used to get covering domains and approximate interfaces, and then maximum likelihood principle of finite mixture model is used to fit each distributing. Experiments indicate that optimization is surely achieved, classification rate is improved and the neural cells are cut down greatly through with proper threshold value.