A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
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
Evolutionary Learning of Modular Neural Networks withGenetic Programming
Applied Intelligence
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Structural Modelling with Sparse Kernels
Machine Learning
Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A Stochastic Optimization Approach for Parameter Tuning of Support Vector Machines
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Evolutionary Radial Basis Functions for Credit Assessment
Applied Intelligence
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Hierarchic Bayesian models for kernel learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
The Genetic Kernel Support Vector Machine: Description and Evaluation
Artificial Intelligence Review
Model-based transductive learning of the kernel matrix
Machine Learning
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
A kernel path algorithm for support vector machines
Proceedings of the 24th international conference on Machine learning
Evolving kernels for support vector machine classification
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving Kernel Functions for SVMs by Genetic Programming
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
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
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Hybrid ensemble approach for classification
Applied Intelligence
Genetic programming for kernel-based learning with co-evolving subsets selection
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Multi-objective model selection for support vector machines
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
Accelerated max-margin multiple kernel learning
Applied Intelligence
Expert Systems with Applications: An International Journal
A distance sum-based hybrid method for intrusion detection
Applied Intelligence
Multi-level rough set reduction for decision rule mining
Applied Intelligence
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Support Vector Machines (SVMs) deliver state-of-the-art performance in real-world applications and are now established as one of the standard tools for machine learning and data mining. A key problem of these methods is how to choose an optimal kernel and how to optimise its parameters. The real-world applications have also emphasised the need to consider a combination of kernels--a multiple kernel--in order to boost the classification accuracy by adapting the kernel to the characteristics of heterogeneous data. This combination could be linear or non-linear, weighted or un-weighted. Several approaches have been already proposed to find a linear weighted kernel combination and to optimise its parameters together with the SVM parameters, but no approach has tried to optimise a non-linear weighted combination. Therefore, our goal is to automatically generate and adapt a kernel combination (linear or non-linear, weighted or un-weighted, according to the data) and to optimise both the kernel parameters and SVM parameters by evolutionary means in a unified framework. We will denote our combination as a kernel of kernels (KoK). Numerical experiments show that the SVM algorithm, involving the evolutionary kernel of kernels (eKoK) we propose, performs better than well-known classic kernels whose parameters were optimised and a state of the art convex linear and an evolutionary linear, respectively, kernel combinations. These results emphasise the fact that the SVM algorithm could require a non-linear weighted combination of kernels.