Work

Selecting from an infinite set of features in SVM

Year: 2011

Type: article

Abstract: . Dealing with the continuous parameters of a feature extraction method has always been a difficult task that is usually solved by cross-validation. In this paper, we propose an active set algorithm for selecting automatically these parameters in a SVM classification context. Our experiments on texture recognition and BCI signal classification show that optimizing the feature parameters in a continuous space while learning the decision function yields to better performances than using fixed parameters obtained from a grid sampling. 1

Authors: Rémi Flamary, Florian Yger, Alain Rakotomamonjy

Institution: Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2406925227

FWCI: 0.787

Cites: 7

Cited by: 6

Related to: 10