The nature of statistical learning theory
The nature of statistical learning theory
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A new co-mutation genetic operator
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
A model for a complex polynomial SVM kernel
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
An evolutionary method for constructing complex SVM kernels
MCBC'09 Proceedings of the 10th WSEAS international conference on Mathematics and computers in biology and chemistry
Determining optimal decision model for support vector machine by genetic algorithm
CIS'04 Proceedings of the First international conference on Computational and Information Science
A general frame for building optimal multiple SVM kernels
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
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In this paper we evaluate the performance of many multiple SVM kernels obtained using a hybrid algorithm. The purpose of our algorithm is to optimize the construction of multiple SVM kernels used in classification tasks. We compare the results obtained using different types of simple kernels and we characterize the behavior of the multiple kernel related to the composition operations +,* and exp and simple kernel types. We use many data sets in order to correlate the performance of our algorithm with the type of the classified data.