Journal
KNOWLEDGE-BASED SYSTEMS
Volume 73, Issue -, Pages 276-288Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2014.10.011
Keywords
Pattern recognition; Support vector machines; Twin support vector machines; Large-scale classification; Weighted linear loss function
Categories
Funding
- National Natural Science Foundation of China [11201426, 11371365]
- Zhejiang Provincial Natural Science Foundation of China [LQ12A01020, LQ13F030010, LQ14G010004]
- Ministry of Education, Humanities and Social Sciences Research Project of China [13YJC910011]
- Scientific Research Fund of Zhejiang Provincial Education Department [Y201432746]
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In this paper, we formulate a twin-type support vector machine for large-scale classification problems, called weighted linear loss twin support vector machine (WLTSVM). By introducing the weighted linear loss, our WLTSVM only needs to solve simple linear equations with lower computational cost, and meanwhile, maintains the generalization ability. So, it is able to deal with large-scale problems efficiently without any extra external optimizers. The experimental results on several benchmark datasets indicate that, comparing to TWSVM, our WLTSVM has comparable classification accuracy but with less computational time. (C) 2014 Elsevier B.V. All rights reserved.
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