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A novel sphere-based maximum margin classification method

conference contribution
posted on 2014-01-01, 00:00 authored by Phuoc NguyenPhuoc Nguyen, Tran Dat, Xu Huang, Wanli Ma
Support vector data description (SVDD) aims at constructing an optimal hypersphere regarded as a data description for a dataset while support vector classification (SVC) aims at separating data of two classes without providing a data description. This paper proposes a unified approach to both SVDD and SVC that aims at separating data of two classes and at the same time provides a data description. A trade off parameter is introduced to control the balance between describing the data and maximising the margin. Experimental results are provided to evaluate the proposed approach.

History

Pagination

620-624

Location

Stockholm, Sweden

Start date

2014-08-24

End date

2014-08-28

ISSN

1051-4651

Language

English

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2014, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

ICPR 2014 : Proceedings of the 2014 22nd International Conference on Pattern Recognition

Event

IEEE Computer Society. Conference (22nd : 2014 : Stockholm, Sweden)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

IEEE Computer Society Conference