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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 MaSupport 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
Event
IEEE Computer Society. Conference (22nd : 2014 : Stockholm, Sweden)Series
IEEE Computer Society ConferencePagination
620 - 624Publisher
Institute of Electrical and Electronics EngineersLocation
Stockholm, SwedenPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-08-24End date
2014-08-28ISSN
1051-4651Language
EnglishPublication classification
E1.1 Full written paper - refereedCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
ICPR 2014 : Proceedings of the 2014 22nd International Conference on Pattern RecognitionUsage metrics
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