The nature of statistical learning theory
The nature of statistical learning theory
Wasp-like Agents for Distributed Factory Coordination
Autonomous Agents and Multi-Agent Systems
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
Evaluation of a hybrid method for constructing multiple SVM kernels
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Mercer’s theorem, feature maps, and smoothing
COLT'06 Proceedings of the 19th annual conference on Learning Theory
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
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The aim of this paper is to define a general frame for building optimal multiple SVM kernels. Our scheme follows 5 steps: formal representation of the multiple kernels, structural representation, choice of genetic algorithm, SVM algorithm, and model evaluation. The computation of the optimal parameter values of SVM kernels is performed using an evolutionary method based on the SVM algorithm for evaluation of the quality of chromosomes. After the multiple kernel is found by the genetic algorithm we apply cross validation method for estimating the performance of our predictive model. We implemented and compared many hybrid methods derived from this scheme. Improved co-mutation operators are used and a comparative study about their effect on the predictive model performances is made. We tested our multiple kernels for classification tasks but they can be also used for other types of tasks.