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

• Type: Article in International Conference Proceedings Book
• Date: 2006

Authors

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

Conference proceedings International

Title: Proceedings of the ACM Symposium on Applied Computing

Pages: 564-568

2006 ACM Symposium on Applied Computing

Dijon, 23 April 2006 through 27 April 2006

Indexing

ISI Web of Knowledge

Scopus - 25 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific