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GECCO
2007
Springer

Evolving kernels for support vector machine classification

9 years 5 months ago
Evolving kernels for support vector machine classification
While support vector machines (SVMs) have shown great promise in supervised classification problems, researchers have had to rely on expert domain knowledge when choosing the SVM's kernel function. This project seeks to replace this expert with a genetic programming (GP) system. Using strongly typed genetic programming and principled kernel closure properties, we introduce a new algorithm, called KGP, which finds near-optimal kernels. The algorithm shows wide applicability, but the combined computational overhead of GP and SVMs remains a major unresolved issue. Categories and Subject Descriptors I.5.1 [Computing Methodologies]: Pattern Recognition--Statistical General Terms Experimentation Keywords Genetic Programming, Support Vector Machines
Keith Sullivan, Sean Luke
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Where GECCO
Authors Keith Sullivan, Sean Luke
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