Abstract
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.
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Bozdogan, H., Camillo, F., Liberati, C. (2006). On the Choice of the Kernel Function in Kernel Discriminant Analysis Using Information Complexity. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_2
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DOI: https://doi.org/10.1007/3-540-35978-8_2
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