@inproceedings{7446995e75f04e6ea37a2ef7964b951f,
title = "A novel sphere-based maximum margin classification method",
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",
keywords = "Maximum margin, Spheres classification, Support vector data description",
author = "Dat TRAN and Xu HUANG and Wanli MA",
year = "2014",
doi = "10.1109/ICPR.2014.117",
language = "English",
isbn = "9781479952090",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "620--624",
editor = "Anders Heyden and Denis Laurendeau and Michael Felsberg",
booktitle = "2014 22nd International Conference on Pattern Recognition",
address = "United States",
note = "22nd International Conference on Pattern Recognition ; Conference date: 24-08-2014 Through 28-08-2014",
}