计算机工程与科学2017，Vol.39Issue(10)：1901-1907,7.DOI:10.3969/j.issn.1007-130X.2017.10.019
一种基于样本加权的合成多核学习方法
A new summation multi-kernel learning method based on sample weighting
摘要
Abstract
Multi-kernel learning is a new research hotspot in current kernel machine learning field.By mapping data into the high dimensional space,kernel methods increase the computing performance of linear classifiers such as support vector machines,and it is a convenient and effective way to deal with nonlinear pattern recognition and classification.However,in some complex situations,such as heterogeneous data or irregular data,large sample size and non-fiat sample distribution,the kernel learning method based on single kernel function cannot completely meet the requirement,so it is necessary to develop multiple kernel functions in order to get better results.We propose a new summation multi-kernel learning method based on sample weighting which can be weighted by the capability of how much a single kernel function can fit the sample.Experiment analysis on several data sets shows that the proposed method can obtain high classification accuracy.关键词
多核学习/映射/非线性模式/数据异构Key words
multi-kernel learning/map/nonlinear model/heterogeneous data分类
信息技术与安全科学引用本文复制引用
沈健,蒋芸,张亚男,胡学伟..一种基于样本加权的合成多核学习方法[J].计算机工程与科学,2017,39(10):1901-1907,7.基金项目
国家自然科学基金(61163036) （61163036）
甘肃省高校研究生导师项目(1201-16) （1201-16）
2012年度甘肃省高校基本科研业务费专项资金 （）
西北师范大学第三期知识与创新工程科研骨干项目(nwnu-kjcxgc-03-67) （nwnu-kjcxgc-03-67）
