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基于差分进化优化的约简最小二乘支持向量机
引用本文:高润鹏,伞冶. 基于差分进化优化的约简最小二乘支持向量机[J]. 哈尔滨工程大学学报, 2011, 0(8): 1012-1018
作者姓名:高润鹏  伞冶
作者单位:哈尔滨工业大学控制与仿真中心;
基金项目:国家自然科学基金资助项目(61074127)
摘    要:
针对最小二乘支持向量回归机的解缺乏稀疏性、预测速度慢等问题,采用向量相关分析在高维特征空间约简支持向量.为使约简模型能最佳逼近原模型,提出原模型与约简模型预测训练样本的平方误差和作为新性能评价准则.为得到最优约简模型,定义了离散加法、减法和乘法算子,并将新性能评价准则作为适应度函数,采用整数编码的差分进化算法进行全局优...

关 键 词:最小二乘支持向量回归机  稀疏性  向量相关分析  差分进化  整数编码  支持向量约简

Reduced least squares support vector machine optimized by differential evolution
GAO Runpeng,SAN Ye. Reduced least squares support vector machine optimized by differential evolution[J]. Journal of Harbin Engineering University, 2011, 0(8): 1012-1018
Authors:GAO Runpeng  SAN Ye
Affiliation:GAO Runpeng,SAN Ye(Control and Simulation Center,Harbin Institute of Technology,Harbin 150001,China)
Abstract:
Aiming at lack of sparseness of the solutions of least squares support vector regression machine which leads to slow prediction speed and other problems,the vector correlation analysis was employed to reduce the support vectors in the high dimensional feature space.In order to make the reduced model best approximate the original one,sum squared prediction errors of training samples between the reduced model and original one were taken as the novel performance evaluation criterion.Discrete addition,subtracti...
Keywords:least squares support vector regression machine  sparseness  vector correlation analysis  differential evolution  integer coded  support vector reduction  
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