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
Computers and Operations Research
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
AI Game Programming Wisdom
Fast Global Optimization of Difficult Lennard-Jones Clusters
Computational Optimization and Applications
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Efficient Algorithms for Large Scale Global Optimization: Lennard-Jones Clusters
Computational Optimization and Applications
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
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
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Solving molecular distance geometry problems by global optimization algorithms
Computational Optimization and Applications
Quantifying the impact of learning algorithm parameter tuning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Evolutionary tuning of multiple SVM parameters
Neurocomputing
An ACO-based algorithm for parameter optimization of support vector machines
Expert Systems with Applications: An International Journal
Support Vector Machines with the Ramp Loss and the Hard Margin Loss
Operations Research
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Gaussian variable neighborhood search for continuous optimization
Computers and Operations Research
Review: Supervised classification and mathematical optimization
Computers and Operations Research
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The default approach for tuning the parameters of a Support Vector Machine (SVM) is a grid search in the parameter space. Different metaheuristics have been recently proposed as a more efficient alternative, but they have only shown to be useful in models with a low number of parameters. Complex models, involving many parameters, can be seen as extensions of simpler and easy-to-tune models, yielding a nested sequence of models of increasing complexity. In this paper we propose an algorithm which successfully exploits this nested property, with two main advantages versus the state of the art. First, our framework is general enough to allow one to address, with the very same method, several popular SVM parameter models encountered in the literature. Second, as algorithmic requirements we only need either an SVM library or any routine for the minimization of convex quadratic functions under linear constraints. In the computational study, we address Multiple Kernel Learning tuning problems for which grid search clearly would be infeasible, while our classification accuracy is comparable to that of ad hoc model-dependent benchmark tuning methods.