A nested heuristic for parameter tuning in Support Vector Machines

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 mod...

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Vydané v:	Computers & operations research Ročník 43; s. 328 - 334
Hlavní autori:	Carrizosa, Emilio, Martín-Barragán, Belén, Romero Morales, Dolores
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 01.03.2014 Elsevier Pergamon Press Inc
Predmet:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial intelligence Computer science; control theory; systems Computer simulation Data processing. List processing. Character string processing Exact sciences and technology Heuristic Learning and adaptive systems Libraries Mathematical functions Mathematical models Mathematical problems Memory organisation. Data processing Multiple kernel learning Nested heuristic Operations research Parameter tuning Searching Software State of the art Studies Supervised classification Support Vector Machines Theoretical computing Tuning Variable neighborhood search Nested heuristic Multiple kernel learning Variable neighborhood search Support Vector Machines Parameter tuning Supervised classification Grid Algorithmics Constraint satisfaction Quadratic programming Modeling Convex programming Parameter space Kernel method Model matching Neighborhood search Heuristic method Vector support machine Learning algorithm Metamodel Quadratic function
ISSN:	0305-0548, 1873-765X, 0305-0548
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Popis
Shrnutí:	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.
Bibliografia:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	0305-0548 1873-765X 0305-0548
DOI:	10.1016/j.cor.2013.10.002