International Journal of Fuzzy Logic and Intelligent Systems

  • 제3권2호
  • /
  • Pages.227-232
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  • 2003
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  • 1598-2645(pISSN)
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  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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Semiparametric Kernel Fisher Discriminant Approach for Regression Problems

  • Park, Joo-Young (Department of Control and Instrumentation Engineering, Korea University) ;
  • Cho, Won-Hee (Department of Control and Instrumentation Engineering, Korea University) ;
  • Kim, Young-Il (Department of Control and Instrumentation Engineering, Korea University)
  • 발행 : 2003.12.01
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초록

Recently, support vector learning attracts an enormous amount of interest in the areas of function approximation, pattern classification, and novelty detection. One of the main reasons for the success of the support vector machines(SVMs) seems to be the availability of global and sparse solutions. Among the approaches sharing the same reasons for success and exhibiting a similarly good performance, we have KFD(kernel Fisher discriminant) approach. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and the KFD approach for regression. After reviewing support vector regression, semi-parametric approach for including predetermined basis functions, and the KFD regression, this paper presents an extension of the conventional KFD approach for regression toward the direction that can utilize predetermined basis functions. The applicability of the presented method is illustrated via a regression example.

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참고문헌

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