Presentation 2005/10/11
Fisher Discriminant with Asymmetric Kernel
Naoya KOIDE, Yukihiko YAMASHITA,
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Abstract(in English) Although the progress of computer with respect to calculation and communication speeds is very rapid, the ability for cognitive processing such as pattern recognition is inferior to that of men. In this research, we apply the asymmetric kernel method to Fisher's linear discriminant and propose the variable kernel Fisher discriminant which can change the kernel parameter according to the position. In the learning stage, the parameter of boundary complexity for the learning sample is optimized. On the other hand the parameter for the unknown input pattern is fixed. Then, the proposed method can manage the case that the discriminant boundary is complex locally. In this paper, we provide the calculation method for Fisher discriminant with the asymmetric kernel and experimental results. We show the advantage of the method by experimental results where the method outperforms KFD for many datasets.
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Keyword(in English) kernel method / asymmetric kernel method / support vector machine (SVM) / kenrel Fisher discriminant
Paper # NC2005-48
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Committee NC
Conference Date 2005/10/11(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Fisher Discriminant with Asymmetric Kernel
Sub Title (in English)
Keyword(1) kernel method
Keyword(2) asymmetric kernel method
Keyword(3) support vector machine (SVM)
Keyword(4) kenrel Fisher discriminant
1st Author's Name Naoya KOIDE
1st Author's Affiliation Graduate School of Sience and Engineering, Tokyo Institute of Technology()
2nd Author's Name Yukihiko YAMASHITA
2nd Author's Affiliation Graduate School of Sience and Engineering, Tokyo Institute of Technology
Date 2005/10/11
Paper # NC2005-48
Volume (vol) vol.105
Number (no) 342
Page pp.pp.-
#Pages 6
Date of Issue