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Conference papers
A method for designing nonlinear Kernel-based discriminant functions from the class of second-order criteria
Abstract : A simple method to derive a nonlinear discriminant is to map samples into a high dimensional space F using a nonlinear function and then to perform a linear discriminant analysis. Using Mercer kernels, this problem can be solved without explicitly mapping into F. Recently, a powerful method of obtaining the nonlinear kernel Fisher discriminant based on Mercer kernels was proposed. Here, we present an extension of this method that consists in determining the optimum nonlinear receiver in the sense of the best second-order criterion, without setting it up. Mercer functions allow obtaining a closed form solution to this problem.
https://hal-utt.archives-ouvertes.fr/hal-02284330
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Wednesday, September 11, 2019 - 4:31:47 PM
Last modification on : Friday, August 27, 2021 - 3:14:06 PM
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Wednesday, September 11, 2019 - 4:31:47 PM
Last modification on : Friday, August 27, 2021 - 3:14:06 PM