Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
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
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Standard kernel-based classifiers use only a single kernel, but the real-world applications and the recent developments of various kernel methods have emphasized the need to consider a combination of multiple kernels. We propose an evolutionary approach for finding the optimal weights of a combined kernel used by the Support Vector Machines (SVM) algorithm for solving some particular problems. We use a genetic algorithm (GA) for evolving these weights. The numerical experiments show that the evolved combined kernels (ECKs) perform better than the convex combined kernels (CCKs) for several classification problems.