火力与指挥控制2017，Vol.42Issue(10)：73-78,6.DOI:10.3969/j.issn.1002-0640.2017.10.016
一种基于Tsallis熵的最小二乘支持向量机稀疏算法
A Sparse Algorithm Based on Tsallis Entropy for Least Squares Support Vector Machine
摘要
Abstract
Least squares support vector machine algorithm is an optimizational support vector machine algorithm. Aiming at the poor sparsity of this algorithm,and the problem of too many support vector,a sparse algorithm based on Tsallis entropy is proposed.Firstly,the training process of the least squares support vector machine algorithm is analyzed.And then the concept of incremental algorithm and Tsallis entropy are put forward.Based on this a solution to solve the sparsity of the algorithm is proposed. Finally,the solution is simulated. The simulation results show that,the improved algorithm has more sparse compared to the traditional algorithm,suitable for system identification in large sample sets.关键词
最小二乘支持向量机/增量算法/稀疏性/Tsallis熵Key words
least squares support vector machines/incremental algorithm/sparsity/tsallis entropy分类
信息技术与安全科学引用本文复制引用
张昌宏,陈元,曹书豪,程思嘉..一种基于Tsallis熵的最小二乘支持向量机稀疏算法[J].火力与指挥控制,2017,42(10):73-78,6.基金项目
全军军事类研究生课题(2013JY430) （2013JY430）
湖北省自然科学基金资助项目(2015CFA066) （2015CFA066）
