@article{DBLP:journals/air/HowleyM05,
title = {The Genetic Kernel Support Vector Machine: Description
and Evaluation},
author = {Tom Howley and Michael G. Madden},
journal = {Artificial Intelligence Review},
number = {3-4},
pages = {379--395},
volume = {24},
year = {2005},
abstract = {The Support Vector Machine (SVM) has emerged in recent
years as a popular approach to the classification of
data. One problem that faces the user of an SVM is how
to choose a kernel and the specific parameters for that
kernel. Applications of an SVM therefore require a
search for the optimum settings for a particular
problem. This paper proposes a classification
technique, which we call the Genetic Kernel SVM (GK
SVM), that uses Genetic Programming to evolve a kernel
for a SVM classifier. Results of initial experiments
with the proposed technique are presented. These
results are compared with those of a standard SVM
classifier using the Polynomial, RBF and Sigmoid kernel
with various parameter settings},
issn = {0269-2821}, bibsource = {DBLP, http://dblp.uni-trier.de}, doi = {doi:10.1007/s10462-005-9009-3},
keywords = {Kernel Kernel, Mercer SVM, algorithms, classification, genetic machine model programming, selection, support vector }
}
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