一种基于粒子群优化的智能模型选择方案

Jingtao Huang, Xiaomei Chi, Jianwei Ma
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引用次数: 1

摘要

为了提高支持向量机的学习效率,提出了一种基于粒子群优化(PSO)的智能模型选择方案来优化超参数。将模型选择问题看作一个多目标优化问题,可以得到一个解集,称为帕累托前;这个集合中的每一个模型都是非劣势的。利用粒子群算法求解上述多目标优化问题,得到模型集。在多个数据集上对该方案进行了测试,结果表明,该方案可以一次得到Pareto front,并且可以更直观地显示各个参数的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An intelligent model selection scheme based on particle swarm optimization
To improve the learning efficiency of support vector machine, an intelligent model selection scheme based on particle swarm optimization (PSO) was presented to optimize the hyper-parameters. By taking the model selection problem as a multi-object optimization problem, one can obtain a solution set known as Pareto front; each one model in this set is non-dominated. PSO was used to solve the above muti-objective optimization problem and then the model set was obtained. The scheme was tested on several datasets, the results show that Pareto front can be obtained in one trial and the effect of every single parameter can be displayed more directly.
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