Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Evolutionary strategies for multi-scale radial basis function kernels in support vector machines
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Evolving kernels for support vector machine classification
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Kernel Trees for Support Vector Machines
IEICE - Transactions on Information and Systems
Experiments on kernel tree support vector machines for text categorization
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
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Finding a kernel mapping function 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 evolutional strategy. To control balance between structure and parameter search towards an optimal kernel, the trade-off strategy is introduced. By experiments on a number of benchmark datasets from UCI and text classification datasets, the proposed method is shown to be able to find a better optimal solution than other search methods.