The nature of statistical learning theory
The nature of statistical learning theory
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
A new co-mutation genetic operator
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
Improving SVM Performance Using a Linear Combination of Kernels
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
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
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Bounded rationality and visual pattern transmission
ECC'11 Proceedings of the 5th European conference on European computing conference
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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The aim of this paper is to present a new method for optimization of SVM multiple kernels The kernel substitution can be used to define many other types of learning machines distinct from SVMs We introduced a new hybrid method which uses in the first level an evolutionary algorithm based on wasp behaviour and on the co-mutation operator LR−Mijn and in the second level a SVM algorithm which computes the quality of chromosomes The most important details of our algorithms are presented The testing and validation proves that multiple kernels obtained using our genetic approach are improving the classification accuracy up to 94.12% for the “leukemia” data set.