A parallel genetic algorithm for solving the inverse problem of Support Vector Machines

被引：0
|
作者：
He, Qiang [1 ]
Wang, Xizhao [1 ]
Chen, Junfen [1 ]
Yan, Leifan [1 ]
机构：
[1] Hebei Univ, Fac Math & Comp Sci, Baoding 071002, Hebei, Peoples R China
来源：
ADVANCES IN MACHINE LEARNING AND CYBERNETICS | 2006年 / 3930卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Support Vector Machines (SVMs) are learning machines that can per-form binary classification (pattern recognition) and real valued function approximation (regression estimation) tasks. An inverse problem of SVMs is how to split a given dataset into two clusters such that the maximum margin between the two clusters is attained. Here the margin is defined according to the separating hyper-plane generated by support vectors. This paper investigates the inverse problem of SVMs by designing a parallel genetic algorithm. Experiments show that this algorithm can greatly decrease time complexity by the use of parallel processing. This study on the inverse problem of SVMs is motivated by designing a heuristic algorithm for generating decision trees with high generalization capability.
引用
收藏
页码：871 / 879
页数：9
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