Journal
ARTIFICIAL INTELLIGENCE REVIEW
Volume 42, Issue 2, Pages 245-252Publisher
SPRINGER
DOI: 10.1007/s10462-012-9336-0
Keywords
Support vector machines; Twin support vector machines; Least squares twin support vector machines; Fuzzy twin support vector machines
Categories
Funding
- National Natural Science Foundation [41074003, 60975039]
- Opening Foundation of the Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences [IIP2010-1]
- Opening Foundation of Beijing Key Lab of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications
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Twin support vector machines (TWSVM) is based on the idea of proximal SVM based on generalized eigenvalues (GEPSVM), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/4 of standard SVM. In addition to keeping the superior characteristics of GEPSVM, the classification performance of TWSVM significantly outperforms that of GEPSVM. However, the stand-alone method requires the solution of two smaller quadratic programming problems. This paper mainly reviews the research progress of TWSVM. Firstly, it analyzes the basic theory and the algorithm thought of TWSVM, then tracking describes the research progress of TWSVM including the learning model and specific applications in recent years, finally points out the research and development prospects.
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