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回归最小二乘支持向量机的增量和在线式学习算法
引用本文:张浩然,汪晓东.回归最小二乘支持向量机的增量和在线式学习算法[J].计算机学报,2006,29(3):400-406.
作者姓名:张浩然  汪晓东
作者单位:浙江师范大学信息科学与工程学院,金华,321004
摘    要:首先给出回归最小二乘支持向量机的数学模型,并分析了它的性质,然后在此基础上根据分块矩阵计算公式和核函数矩阵本身的特点设计了支持向量机的增量式学习算法和在线学习算法.该算法能充分利用历史的训练结果,减少存储空间和计算时间.仿真实验表明了这两种学习方法的有效性.

关 键 词:结构风险最小化  最小二乘支持向量机  在线学习
收稿时间:2004-04-04
修稿时间:2004-04-042005-11-24

Incremental and Online Learning Algorithm for Regression Least Squares Support Vector Machine
ZHANG Hao-Ran,WANG Xiao-Dong.Incremental and Online Learning Algorithm for Regression Least Squares Support Vector Machine[J].Chinese Journal of Computers,2006,29(3):400-406.
Authors:ZHANG Hao-Ran  WANG Xiao-Dong
Affiliation:College of Information Science and Engineering, Zhejiang Normal University, Jinhua 321004
Abstract:Support vector machine is a learning technique based on the structural risk minimization principle,and it is also a class of regression method with good generalization ability.The paper firstly introduces the mathematical model of regression least squares support vector machine(LSSVM),and analyzes its property,then designs incremental and online learning algorithms based on LSSVM by the calculation formula of block matrix and kernel function matrix's property.The proposed learning algorithms fully utilizes the historical training results,reduces storage space and calculate time.Experimental results of simulation indicate the feasibility of the two learning algorithms.
Keywords:structural risk minimization  least squares support vector machine  online learning
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