A Family-Based Evolutional Approach for Kernel Tree Selection in SVMs
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概要
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Finding a kernel mapping function for support vector machines (SVMs) is a key step towards construction of a high-performanced SVM-based classifier. While some recent methods exploited an evolutional approach to construct a suitable multifunction kernel, most of them searched randomly and diversely. In this paper, the concept of a family of identical-structured kernel trees is proposed to enable exploration of structure space using genetic programming whereas to pursue investigation of parameter space on a certain tree using evolution strategy. To control balance between structure and parameter search towards an optimal kernel, simulated annealing is introduced. By experiments on a number of benchmark datasets in the UCI and text classification collection, the proposed method is shown to be able to find a better optimal solution than other search methods, including grid search and gradient search.
- 2010-04-01
著者
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Theeramunkong Thanaruk
Information And Computer Technology School Sirindhorn International Institute Of Technology Thammasa
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METHASATE Ithipan
Information and Computer Technology School, Sirindhorn International Institute of Technology, Thamma
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Methasate Ithipan
Information And Computer Technology School Sirindhorn International Institute Of Technology Thammasa
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