An ACO-based algorithm for parameter optimization of support vector machines

被引：216

作者：

Zhang, XiaoLi ^{[1
]}

Chen, XueFeng ^{[1
]}

He, ZhengJia ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2010年 / 37卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Ant colony optimization (ACO) algorithm; Support vector machines (SVM); Parameter optimization; ACO-SVM model; ANT COLONY OPTIMIZATION; GENETIC ALGORITHM; FAULT-DIAGNOSIS; SELECTION;

D O I：

10.1016/j.eswa.2010.03.067

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

One of the significant research problems in support vector machines (SVM) is the selection of optimal parameters that can establish an efficient SVM so as to attain desired output with an acceptable level of accuracy. The present study adopts ant colony optimization (ACO) algorithm to develop a novel ACO-SVM model to solve this problem. The proposed algorithm is applied on some real world benchmark datasets to validate the feasibility and efficiency, which shows that the new ACO-SVM model can yield promising results. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：6618 / 6628

页数：11

共 33 条

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Optimizing resources in model selection for support vector machine [J].