计算机科学 ›› 2016, Vol. 43 ›› Issue (Z6): 77-80.doi: 10.11896/j.issn.1002-137X.2016.6A.017
陈晓青,陆慧娟,关伟,郑文斌
CHEN Xiao-qing, LU Hui-juan, GUAN Wei and ZHENG Wen-bin
摘要: 在分析核极限学习机原理的基础上,将小波函数作为核函数运用于极限学习机中,形成小波核极限学习机(WKELM)。实验表明,该算法提高了分类性能,增加了鲁棒性。在此基础上利用探测粒子群(Detecting Particle Swarm Optimization,DPSO)对WKELM参数优化,最终得到分类效果较优的DPSO-WKELM分类器。通过采用UCI基因数据进行仿真,将该分类结果与径向基核极限学习机(KELM)、WKELM等算法结果进行比较,得出所提算法具有较高的分类精度。
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