Sparse Least Square Support Vector Machines based on Random Entropy

被引：0

作者：

Ma, Wenlu ^{[1
]}

Liu, Han ^{[2
]}

机构：

[1] Xian Univ Technol, Fac Automat & Informat Engn, Xian 710048, Shaanxi, Peoples R China

[2] Xian Univ Technol, Fac Automat & Informat Engn, Shaanxi Key Lab Complex Syst Control & Intelligen, Xian 710048, Shaanxi, Peoples R China

来源：

PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) | 2017年

基金：

中国国家自然科学基金;

关键词：

Sparsity LSSVM; Large-scale data; Random Entropy;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Least squares support vector machines (LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method is used to the sparse samples in each subset. Finally, we use sparse samples sets as training samples, and use least squares support vector machine algorithm to train. The results show that the sparse least squares support vector machine model based on entropy can effectively solve the problem of large-scale data.

引用

页码：333 / 337

页数：5

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