Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2006

Autores

Soares, C (Autor) FEP; Brazdil, PB (Autor) FEP

Ata de Conferência Internacional

Título: Proceedings of the ACM Symposium on Applied Computing

Páginas: 564-568

2006 ACM Symposium on Applied Computing

Dijon, 23 April 2006 through 27 April 2006

Indexação

ISI Web of Knowledge

Scopus - 25 Citações

Outras Informações

ID Authenticus: P-007-GKF

DOI: 10.1145/1141277.1141408

Abstract (EN): The Support Vector Machine (SVM) algorithm is sensitive to the choice of parameter settings, which makes it hard to use by non-experts. It has been shown that meta-learning can be used to support the selection of SVM parameter values. Previous approaches have used general statistical measures as meta-features. Here we propose a new set of meta-features that are based on the kernel matrix. We test them on the problem of setting the width of the Gaussian kernel for regression problems. We obtain significant improvements in comparison to earlier meta-learning results. We expect that with better support in the selection of parameter values, SVM becomes accessible to a wider range of users. Copyright 2006 ACM.

Idioma: Inglês

Tipo (Avaliação Docente): Científica