A novel sphere-based maximum margin classification method

Dat TRAN, Xu HUANG, Wanli MA

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Abstract

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
Original languageEnglish
Title of host publication2014 22nd International Conference on Pattern Recognition
EditorsAnders Heyden, Denis Laurendeau, Michael Felsberg
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages620-624
Number of pages5
ISBN (Electronic)9781479952083
ISBN (Print)9781479952090
DOIs
Publication statusPublished - 2014
Event22nd International Conference on Pattern Recognition - Stockholm, Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference22nd International Conference on Pattern Recognition
CountrySweden
CityStockholm
Period24/08/1428/08/14

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Data description

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TRAN, D., HUANG, X., & MA, W. (2014). A novel sphere-based maximum margin classification method. In A. Heyden, D. Laurendeau, & M. Felsberg (Eds.), 2014 22nd International Conference on Pattern Recognition (pp. 620-624). [6976827] (Proceedings - International Conference on Pattern Recognition). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICPR.2014.117
TRAN, Dat ; HUANG, Xu ; MA, Wanli. / A novel sphere-based maximum margin classification method. 2014 22nd International Conference on Pattern Recognition. editor / Anders Heyden ; Denis Laurendeau ; Michael Felsberg. USA : IEEE, Institute of Electrical and Electronics Engineers, 2014. pp. 620-624 (Proceedings - International Conference on Pattern Recognition).
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abstract = "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",
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TRAN, D, HUANG, X & MA, W 2014, A novel sphere-based maximum margin classification method. in A Heyden, D Laurendeau & M Felsberg (eds), 2014 22nd International Conference on Pattern Recognition., 6976827, Proceedings - International Conference on Pattern Recognition, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 620-624, 22nd International Conference on Pattern Recognition, Stockholm, Sweden, 24/08/14. https://doi.org/10.1109/ICPR.2014.117

A novel sphere-based maximum margin classification method. / TRAN, Dat; HUANG, Xu; MA, Wanli.

2014 22nd International Conference on Pattern Recognition. ed. / Anders Heyden; Denis Laurendeau; Michael Felsberg. USA : IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 620-624 6976827 (Proceedings - International Conference on Pattern Recognition).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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TRAN D, HUANG X, MA W. A novel sphere-based maximum margin classification method. In Heyden A, Laurendeau D, Felsberg M, editors, 2014 22nd International Conference on Pattern Recognition. USA: IEEE, Institute of Electrical and Electronics Engineers. 2014. p. 620-624. 6976827. (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2014.117