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Polynomial kernel adaptation and extensions to the SVM classifier learning

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Abstract

Three extensions to the Kernel-AdaTron training algorithm for Support Vector Machine classifier learning are presented. These extensions allow the trained classifier to adhere more closely to the constraints imposed by Support Vector Machine theory. The results of these modifications show improvements over the existing Kernel-AdaTron algorithm. A method of parameter optimisation for polynomial kernels is also proposed.

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Authors and Affiliations

  1. Mechatronics Research Group, University of Melbourne, Melbourne, VIC, 3010, Australia

    Ramy Saad, Saman K. Halgamuge & Jason Li

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  1. Ramy Saad
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  2. Saman K. Halgamuge
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  3. Jason Li
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Correspondence to Jason Li.

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Saad, R., Halgamuge, S.K. & Li, J. Polynomial kernel adaptation and extensions to the SVM classifier learning. Neural Comput & Applic 17, 19–25 (2008). https://doi.org/10.1007/s00521-006-0078-2

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  • DOI: https://doi.org/10.1007/s00521-006-0078-2

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