On Kernel Discriminant Analyses Applied to Phoneme Classification | Zendy

András Kocsor | Zendy

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On Kernel Discriminant Analyses Applied to Phoneme Classification

Author(s) -

András Kocsor

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-25913-9

DOI - 10.1007/11427445_58

Subject(s) - kernel fisher discriminant analysis , kernel (algebra) , linear discriminant analysis , pattern recognition (psychology) , computer science , artificial intelligence , variable kernel density estimation , kernel method , discriminant , radial basis function kernel , kernel embedding of distributions , tree kernel , multiple discriminant analysis , polynomial kernel , mathematics , support vector machine , discrete mathematics

In this paper we recall two kernel methods for discriminant analysis. The first one is the kernel counterpart of the ubiquitous Linear Discriminant Analysis (Kernel-LDA), while the second one is a method we named Kernel Springy Discriminant Analysis (Kernel-SDA). It seeks to separate classes just as Kernel-LDA does, but by means of defining attractive and repulsive forces. First we give technical details about these methods and then we employ them on phoneme classification tasks. We demonstrate that the application of kernel functions significantly improves the recognition accuracy.

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