期刊
INFORMATION SCIENCES
卷 263, 期 -, 页码 22-35出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2013.11.003
关键词
Pattern recognition; Binary classification; Support vector machines; Proxirrial classifiers; Nonparallel hyperplanes
资金
- National Natural Science Foundation of China [10971223, 11161045, 11201426, 11371365]
- Zhejiang Provincial Natural Science Foundation of China [LQ12A01020, LQ13F030010]
In this paper, we propose a nonparallel hyperplane support vector machine (NHSVM) for binary classification problems. Our proposed NHSVM is formulated by clustering the training points according to the similarity between classes. It constructs two nonparallel hyperplanes simultaneously by solving a single quadratic programming problem, and is consistent between its predicting and training processes - an essential difference that distinguishes it from other nonparallel SVMs. This proposed NHSVM has been analyzed theoretically and implemented experimentally. The results of experiments conducted using it on both artificial and publicly available benchmark datasets confirm its feasibility and efficacy, especially for Cross Planes datasets and datasets with heteroscedastic noise. Crown Copyright (C) 2013 Published by Elsevier Inc. All rights reserved.
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