dc.contributor.author | Madden, Michael G. | en |
dc.contributor.author | Howley, Tom | en |
dc.date.accessioned | 2009-05-13T09:31:52Z | en |
dc.date.available | 2009-05-13T09:31:52Z | en |
dc.date.issued | 2004 | en |
dc.identifier.citation | "The Genetic Evolution of Kernels for Support Vector Machine Classifiers" , Tom Howley and Michael G. Madden. Proceedings of AICS-2004, 15th Irish Conference on Artificial Intelligence & Cognitive Science, September 2004. | en |
dc.identifier.uri | http://hdl.handle.net/10379/185 | en |
dc.description.abstract | Abstract. The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classi¿cation of data. One problem that faces the user of an SVM is how to choose a kernel and the speci¿c parameters for that kernel.Applications of an SVM therefore require a search for the optimum settings for aparticular problem. This paper proposes a classi¿cation technique, which we call the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve akernel for a SVMclassi¿er. Results of initial experiments with the proposed tech-nique are presented. These results are compared with those of a standard SVM classi¿er using the Polynomial or RBF kernel with various parameter settings. | en |
dc.format | application/pdf | en |
dc.language.iso | en | en |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Ireland | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ie/ | |
dc.subject | Support vector machines | en |
dc.subject | Genetic kernel (GK SVM) | en |
dc.subject | Genetic programming | en |
dc.subject | SVM classifier | en |
dc.subject | Polynomial kernel | en |
dc.subject.lcsh | Support vector machines | en |
dc.subject.lcsh | Kernel functions | en |
dc.subject.lcsh | Genetic programming (Computer science) | en |
dc.subject.lcsh | Polynomials | en |
dc.title | The Genetic Evolution of Kernels for Support Vector Machine Classifiers | en |
dc.type | Conference Paper | en |
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