Training Least-Square SVM by a Recurrent Neural Network Based on Fuzzy c-mean Approach

摘要：

　　An algorithm to solve the least square support vector ma chine (LSSVM) is presented.The underlying optimization problem for LSSVM follows a system of linear equations.The proposed algorithm incorporates a fuzzy c-mean (FCM) clustering approach and the appli cation of a recurrent neural network (RNN) to solve the system of linear equations.First, a reduced training set is obtained by the FCM clustering approach and used to train LSSVM.Then a gradient system with dis continuous righthand side, interpreted as an RNN, is designed by using the corresponding system of linear equations.The fusion of FCM cluster ing approach and RNN overcomes the loss of spareness of LSSVM.The efficiency of the algorithm is empirically shown on a benchmark data set generated from the University of California at Irvine (UCI) machine learning database.

关键词： least square support vector machine neural network fuzzy c-mean clustering

作者: Fengqiu Liu Jianmin Wang Sitian Qin

作者单位: Harbin University of Science and Technology Harbin Institute of Technology, Harbin, 150080, China

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 106-113

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）