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http://dx.doi.org/10.5302/J.ICROS.2004.10.2.185

Non-linear Data Classification Using Partial Least Square and Residual Compensator  

김경훈 (울산대학교 전기전자정보시스템공학부)
김태영 (알칸 대한 주식회사)
최원호 (울산대학교 전기전자정보시스템공학부)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.10, no.2, 2004 , pp. 185-191 More about this Journal
Abstract
Partial least squares(PLS) is one of multiplicate statistical process methods and has been developed in various algorithms with the characteristics of principal component analysis, dimensionality reduction, and analysis of the relationship between input variables and output variables. But it has been limited somewhat by their dependency on linear mathematics. The algorithm is proposed to classify for the non-linear data using PLS and the residual compensator(RC) based on radial basis function network (RBFN). It compensates for the error of the non-linear data using the RC based on RBFN. The experimental result is given to verify its efficiency compared with those of previous works.
Keywords
partial least squares; radial basis function network; residual compensator.;
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