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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Veröffentlicht in:	Computers & operations research Jg. 43; S. 328 - 334
Hauptverfasser:	Carrizosa, Emilio, Martín-Barragán, Belén, Romero Morales, Dolores
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.03.2014 Elsevier Pergamon Press Inc
Schlagworte:	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
Online-Zugang:	Volltext
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