Model selection for SVM classification based on kernel prototype and adaptive genetic algorithm

doi:10.7523/j.issn.2095-6134.2012.1.009

Abstract

Abstract:

Model selection plays an important role in support of vector machine classification. We propose a concept of kernel prototype which means general kernel type. Based on this, a new algorithm for model selection for supporting vector machine classification is proposed. The powerful adaptive genetic algorithm is used to optimize the parameters in kernel prototype, and it can find the optimal kernel type as well as the kernel parameters. Experiments show effectiveness and stability of the algorithms.

Key words: support vector machine (SVM), adaptive genetic algorithms (AGA), model selection, scene classification, remote sensing image

CLC Number:

TP751

CHEN Gang, WANG Hong-Qi, SUN Xian. Model selection for SVM classification based on kernel prototype and adaptive genetic algorithm[J]. , 2012, 29(1): 62-69.

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