Optimizing parameters of support vector machines using team-search-based particle swarm optimization

Zhang Long; Wang Jianhua

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Optimizing parameters of support vector machines using team-search-based particle swarm optimization

机译：使用基于团队搜索的粒子群算法优化支持向量机的参数

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摘要

Purpose - It is greatly important to select the parameters for support vector machines (SVM), which is usually determined by cross-validation. However, the cross-validation is very time-consuming and complicated to create good parameters for SVM. The parameter tuning issue can be solved in the optimization framework. The paper aims to discuss these issues.

机译：目的-选择支持向量机（SVM）的参数非常重要，该参数通常由交叉验证确定。但是，交叉验证非常耗时且复杂，无法为SVM创建良好的参数。参数调整问题可以在优化框架中解决。本文旨在讨论这些问题。

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来源
《Engineering Computations》 |2015年第5期|1194-1213|共20页
作者
Zhang Long; Wang Jianhua;
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作者单位

Harbin Normal Univ, Coll Comp Sci & Informat Engn, Harbin, Peoples R China;

Harbin Normal Univ, Coll Comp Sci & Informat Engn, Harbin, Peoples R China|Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150006, Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Parameter optimization; Dynamic inertia weight; Particle swarm optimization; Support vector machine; Team-based search strategy;

机译：参数优化;动态惯性权重;粒子群优化;支持向量机;基于团队的搜索策略;

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Optimizing parameters of support vector machines using team-search-based particle swarm optimization

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