A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
The Genetic Kernel Support Vector Machine: Description and Evaluation
Artificial Intelligence Review
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Determining optimal decision model for support vector machine by genetic algorithm
CIS'04 Proceedings of the First international conference on Computational and Information Science
An evolutionary approach to automatic kernel construction
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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
Evaluation of a hybrid method for constructing multiple SVM kernels
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Optimization of complex SVM kernels using a hybrid algorithm based on wasp behaviour
LSSC'09 Proceedings of the 7th international conference on Large-Scale Scientific Computing
A general frame for building optimal multiple SVM kernels
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
Computers in Biology and Medicine
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SVM models are obtained by convex optimization and are able to learn and generalize in high dimensional input spaces. The kernel method is a very powerful idea. Using an appropriate kernel, the data are projected in a space with higher dimension in which they are separable by an hyperplane. Usually simple kernels are used but the real problems require more complex kernels. The aim of this paper is to introduce and analyze a multiple kernel based only on simple polynomials kernels.