Support vector machines with different norms: motivation, formulations and results

• Type: Article in International Scientific Journal
• Date: 2001

Authors

Pedroso, JP (Author) FCUP; Murata, N (Author) Other

Journal

Title: Pattern Recognition Letters

Vol. 22 No. 12

Pages: 1263-1272

ISSN: 0167-8655

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 31 Citations

Scopus - 33 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-000-T6Z

DOI: 10.1016/s0167-8655(01)00071-x

Abstract (EN): We introduce two formulations for training support vector machines, based on considering the L-1 and L-infinity norms instead of the currently used L-2 norm, and maximising the margin between the separating hyperplane and each data sets using L-1 and L-infinity distances. We exploit the geometrical properties of these different norms. and propose what kind of results should be expected for them. Formulations in mathematical programming for linear problems corresponding to L-1 and L-infinity norms are also provided, for both the separable and non-separable cases. We report results obtained for some standard benchmark problems. which confirmed that the performance of all the formulations is similar. As expected, the CPU time required for machines solvable with linear programming is much shorter.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10