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On Kernel Discriminant Analyses Applied to Phoneme Classification

  • András Kocsor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3497)

Abstract

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.

Keywords

Discriminant Analysis Linear Discriminant Analysis Gaussian Mixture Model Phonological Awareness Kernel Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • András Kocsor
    • 1
  1. 1.Research Group on Artificial IntelligenceHungarian Academy of Sciences and University of SzegedSzegedHungary

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