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《Control and Decision》 2006-01
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AlignLoo Model Selection Approach for Regularized Least-squares Classification

YANG Hui-hua~(1,2),WANG Xing-yu~1,WANG Yong~(1,3),GAO Haihua~1(1.College of Information Science and Engineering,East China University of Science & Technology,Shanghai 200237,China;2.Department of Computer, Guilin University of Electronic Technology,Guilin 541004,China;3.Network Information Center,Guilin University of Electronic Technology,Guilin 541004,China.  
Regularized Least-Squares Classification(RLSC) is a kind of kernel-based square loss regularization network,and its generalization performance is severely influenced by the setting of its kernel and hyper parameters.A novel approach named AlignLoo is presented for optimal parameters selection,and the key of this approach lies in tuning the kernel parameter and hyper parameter sequentially.First,the optimal kernel parameter is selected by maximizing kernel target alignment,and then the optimal hyper-parameter is determined via minimizing RLSC's leave-one-out error bound.The experimental results on IDA benchmark datasets demonstrate that the proposed approach is superior to traditional grid search method in that it is much more time efficient and it needs no independent validation set.
【Fund】: 国家重点基础研究发展规划项目(2002CB312200);; 高等学校博士点基金项目(20040251010)
【CateGory Index】: O241.5
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1 Yu Guo-xian① Zhang Guo-ji② Wei Jia① Ren Ya-zhou① ①(School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,China) ②(School of Sciences,South China University of Technology,Guangzhou 510640,China);A Multi Graphs Based Transductive Ensemble Classification Method[J];Journal of Electronics & Information Technology;2011-08
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