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17

NCA
2008
IEEE

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Polynomial kernel adaptation and extensions to the SVM classifier learning

13 years 4 months ago

Polynomial kernel adaptation and extensions to the SVM classifier learning

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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. Keywords Kernel methods

Ramy Saad, Saman K. Halgamuge, Jason Li

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Computer Networks | Kernel-AdaTron Algorithm | Kernel-AdaTron Training Algorithm | NCA 2008 | Support Vector Machines |

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Added	14 Dec 2010
Updated	14 Dec 2010
Type	Journal
Year	2008
Where	NCA
Authors	Ramy Saad, Saman K. Halgamuge, Jason Li

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