Data classification based on the hybrid versions of the particle swarm optimization algorithm

机译：基于混合版本的粒子群算法的数据分类

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The paper presents two hybrid versions of the basic PSO algorithm, involving the use of the classical Grid Search (GS) algorithm and Design of Experiment (DOE) algorithm correspondingly. These hybrid versions have been applied to the problem of search of the parameters values of the SVM classifier. The results of experimental studies confirm the application efficiency of the hybrid versions of the basic PSO algorithm with the aim of reducing of the time expenditures for searching the optimum parameters of the SVM classifier while maintaining of high quality of its classification decisions.

机译：本文介绍了基本PSO算法的两个混合版本，分别涉及经典网格搜索（GS）算法和实验设计（DOE）算法的使用。这些混合版本已应用于搜索SVM分类器的参数值的问题。实验研究的结果证实了基本PSO算法的混合版本的应用效率，其目的是减少用于搜索SVM分类器最佳参数的时间开销，同时保持其分类决策的高质量。

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《Mediterranean Conference on Embedded Computing》|2018年|1-4|共4页
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Liliya Demidova; Irina Klyueva;
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Classification algorithms; Support vector machines; Search problems; Machine learning algorithms; Optimization; Linear programming; Shape;

机译：分类算法;支持向量机;搜索问题;机器学习算法;优化;线性规划;形状;

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Data classification based on the hybrid versions of the particle swarm optimization algorithm

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