The Genetic Kernel Support Vector Machine: Description and Evaluation

The Genetic Kernel Support Vector Machine: Description and Evaluation (2005)

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by Tom Howley , Michael G. Madden

Venue:	Artificial Intelligence Review
Citations:	18 - 0 self

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@ARTICLE{Howley05thegenetic,
    author = {Tom Howley and Michael G. Madden},
    title = {The Genetic Kernel Support Vector Machine: Description and Evaluation},
    journal = {Artificial Intelligence Review},
    year = {2005},
    volume = {24},
    pages = {379--395}
}

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Abstract

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.

Citations

2960	Artificial Intelligence: A Modern Approach. 2nd edn - Russell, Norvig - 2003
2528	UCI Repository of Machine Learning Databases - Blake, Keogh, et al. - 1998
1430	The Elements of Statistical Learning - Hastie, Tibshirani, et al. - 2009
936	A training algorithm for optimal margin classifiers - Boser, Guyon, et al. - 1992
805	Genetic Programming - Koza, Rice - 1992
743	An Introduction to Support Vector Machines - Cristianini, Shawe-Taylor - 2000
454	Training support vector machines: an application to face detection - Osuna, Freund, et al. - 1997
282	Text classification using string kernels - Lodhi, Saunders, et al. - 2002
263	A Practical Guide to Support Vector Classification,” http://www.csie.ntu.edu.tw/˜cjlin/papers/guide/guide.pdf - Hsu, Chang, et al. - 2003
234	Genetic Programming – An Introduction - Banzhaf, Nordin, et al. - 1998
60	H.: On-line handwriting recognition with support vector machines - a kernel approach - Bahlmann, Haasdonk, et al. - 2002
46	Dynamically adapting kernels in support vector machines - Cristianini, Campbell, et al. - 1999
41	Scholkopf B (2003) Feature Selection for Support Vector Machines by Means of Genetic Algorithms - Fröhlich, Chapelle
41	A study on sigmoid kernels for svm and the training of non-psd kernels by smo-type methods - Lin, Lin - 2003
35	Evolutionary tuning of multiple SVM parameters, in: M. Verleysen (Ed - Friedrichs, Igel - 2004
32	Using analytic QP and sparseness to speed training of support vector machines. Advances in neural information processing systems - Platt - 1999
27	Genetic Algorithms and Support Vector Machines for Time Series Classification - Eads, Hill, et al.
20	Asynchronous parallel evolutionary model selection for support vector machines - Runarsson, Sigurdsson
18	Statistical Learning and Kernel Methods - Schölkopf, Guyon, et al. - 2003
17	Parameter selection for support vector machines - Staelin
11	Text categorisation with support vector machines: Learning with many relevant features - Joachims - 1998
3	Epothilones: Quantitative Structure Activity Relations Studied by Support Vector - Bleckmann, Meiler
3	Using SVMs for text categorisation - Hearst - 1998
3	An improved genetic programming technique for the classification of Raman spectra. KnowledgeBased Systems - Hennessy, Madden, et al. - 2005
3	Support Vector Machines - a practical consequence of learning theory - Scholkopf - 1998