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On Kernel Discriminant Analyses Applied to Phoneme Classification

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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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.

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Author information

Authors and Affiliations

  1. Research Group on Artificial Intelligence, Hungarian Academy of Sciences and University of Szeged, H-6720, Szeged, Aradi vértanúk tere 1, Hungary

    András Kocsor

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  1. András Kocsor
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Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Kocsor, A. (2005). On Kernel Discriminant Analyses Applied to Phoneme Classification. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_58

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  • DOI: https://doi.org/10.1007/11427445_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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