Least Squares Twin Support Vector Machine for Multi-Class Classification
نویسندگان
چکیده
Twin support vector machine (TWSVM) was initially designed for binary classification. However, real-world problems often require the discrimination more than two categories. To tackle multi-class classification problem, in this paper, a multiple least squares twin support vector machine is proposed. Our Multi-LSTSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each problem is similar to binary LSTSVM. Comparison against the Multi-LSSVM, Multi-GEPSVM, Multi-TWSVM and our Multi-LSTSVM on both UCI datasets and ORL, YALE face datasets illustrate the effectiveness of the proposed method.
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Article history: Received 9 September 2014 Received in revised form 25 January 2015 Accepted 9 February 2015 Available online xxxx
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