西安电子科技大学学报（自然科学版）Issue(1)：36-40,5.DOI:10.3969/j.issn.1001-2400.2016.01.007
快速随机多核学习分类算法
Fast randommultiple kernel learning for classification
摘要
Abstract
Multiple kernel learning ( MKL) combines multiple kernels in a convex optimization framework and seeks the best line combination of them . Generally , MKL can get better results than single kernel learning , but heavy computational burden makes MKL impractical . Inspired by the extreme learning machine ( ELM ) , a novel fast MKL method based on the random kernel is proposed . When the framework of ELM is satisfied , the kernel parameters can be given randomly , which produces the random kernel . Thus , the sub-kernel scale is reduced largely , which accelerates the training time and saves the memory . Furthermore , the reduced kernel scale can reduce the error bound of MKL by analyzing the empirical Rademacher complexity of MKL . It gives a theoretical guarantee that the proposed method gets a higher classification accuracy than traditional MKL methods . Experiments indicate that the proposed method uses a faster speed , more small memory and gets better results than several classical fast MKL methods .关键词
多核学习/极限学习/随机核/经验 Rademacher 复杂度Key words
multiple kernel learning/extreme learning machine/random kernel/empirical rademacher complexity分类
信息技术与安全科学引用本文复制引用
孙涛,冯婕..快速随机多核学习分类算法[J].西安电子科技大学学报（自然科学版）,2016,(1):36-40,5.基金项目
国家973计划资助项目（2013CB329402）；国家自然科学基金资助项目（61272282）；新世纪人才计划资助项目 （）
