Hypersphere support vector machines based on generalized multiplicative updates  

基于广义乘性规则的支持向量机(英文)

作　　者：吴青 刘三阳 张乐友 

机构地区：[1]Department of Mathematical Sciences,Xidian University

出　　处：《Journal of Shanghai University(English Edition)》2008年第2期126-130,共5页上海大学学报（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (Grant No.60574075)

摘　　要：This paper proposes a novel hypersphere support vector machines （HSVMs） based on generalized multiplicative updates. This algorithm can obtain the boundary of hypersphere containing one class of samples by the description of the training samples from one class and use this boundary to classify the test samples. The generalized multiplicative updates are applied to solving boundary optimization progranmning. Multiplicative updates available are suited for nonnegative quadratic convex programming. The generalized multiplicative updates are derived to box and sum constrained quadratic programming in this paper. They provide an extremely straightforward way to implement support vector machines （SVMs） where all variables are updated in parallel. The generalized multiplicative updates converge monotonically to the solution of the maximum margin hyperplane. The experiments show the superiority of our new algorithm.This paper proposes a novel hypersphere support vector machines （HSVMs） based on generalized multiplicative updates. This algorithm can obtain the boundary of hypersphere containing one class of samples by the description of the training samples from one class and use this boundary to classify the test samples. The generalized multiplicative updates are applied to solving boundary optimization progranmning. Multiplicative updates available are suited for nonnegative quadratic convex programming. The generalized multiplicative updates are derived to box and sum constrained quadratic programming in this paper. They provide an extremely straightforward way to implement support vector machines （SVMs） where all variables are updated in parallel. The generalized multiplicative updates converge monotonically to the solution of the maximum margin hyperplane. The experiments show the superiority of our new algorithm.

关 键 词：hypersphere support vector machines （HSVMs） multiplicative updates sum and box constrained quadraticprogramming classification. 

分 类 号：O234[理学—运筹学与控制论]