title: Tabu search model selection for SVM.
abstract: A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. The aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. The selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times.
authors.affiliation: Université de Caen Basse-Normandie, Marechal Juin, Caen, F-14050, France. gilles.lebrun@unicaen.fr
authors.forename: Gilles
authors.initials: G
authors.lastname: Lebrun
authors.forename: Christophe
authors.initials: C
authors.lastname: Charrier
authors.forename: Olivier
authors.initials: O
authors.lastname: Lezoray
authors.forename: Hubert
authors.initials: H
authors.lastname: Cardot
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D003661:Decision Support Techniques; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016247:Information Storage and Retrieval; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated