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On the choice of the kernel function in kernel discriminant analysis using information complexity. (English) Zbl 1435.62227

Zani, Sergio (ed.) et al., Data analysis, classification and the forward search. Proceedings of the meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, Parma, Italy, June 6–8, 2005. Berlin: Springer. Stud. Classification Data Anal. Knowl. Organ., 11-21 (2006).
Summary: In this short paper we shall consider the Kernel Fisher Discriminant Analysis (KFDA) and extend the idea of Linear Discriminant, Analysis (LDA) to nonlinear feature space. We shall present a new method of choosing the optimal kernel function and its effect on the KDA classifier using information-theoretic complexity measure.
For the entire collection see [Zbl 1097.62504].

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62B10 Statistical aspects of information-theoretic topics
94A17 Measures of information, entropy

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