Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Adaptive integration using evolutionary strategies
HIPC '96 Proceedings of the Third International Conference on High-Performance Computing (HiPC '96)
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
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In support vector machines (SVMs), kernel functions are used to compute dot product in a higher dimensional feature space. The performance of classification depends on the chosen kernel. The radial basis function (RBF) kernel is a most popular kernel that succeeded in many tasks. In order to obtain a more flexible kernel function, a family of RBF kernels is proposed. Multi-scale RBF kernels are combined by including weights. This new kernel is proved to be a Mercer's kernel. Then, the evolutionary strategies (ESs) are used to adjust the weights and the widths of the RBF kernels. The training accuracy, the bound of generalization error, and subsets cross-validation are used to be objective functions in the evolutionary process. The experimental results show that the proposed kernel allows better discrimination in the feature space. Moreover, subsets cross-validation is a good objective function and yields the effective results on benchmarks.