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Title: An extended Lagrangian support vector machine for classifications
Authors: Yang, X. 
Shu, L.
Hao, Z.
Liang, Y.
Liu, G. 
Han, X. 
Keywords: Decomposition algorithm
Quadratic programming
Support vector machine
Issue Date: Jun-2004
Citation: Yang, X.,Shu, L.,Hao, Z.,Liang, Y.,Liu, G.,Han, X. (2004-06). An extended Lagrangian support vector machine for classifications. Progress in Natural Science 14 (6) : 519-523. ScholarBank@NUS Repository.
Abstract: Lagrangian support vector machine (LSVM) cannot solve large problems for nonlinear kernel classifiers. In order to extend the LSVM to solve very large problems, an extended Lagrangian support vector machine (ELSVM) for classifications based on LSVM and SVMlight is presented in this paper. Our idea for the ELSVM is to divide a large quadratic programming problem into a series of subproblems with small size and to solve them via LSVM. Since the LSVM can solve small and medium problems for nonlinear kernel classifiers, the proposed ELSVM can be used to handle large problems very efficiently. Numerical experiments on different types of problems are performed to demonstrate the high efficiency of the ELSVM.
Source Title: Progress in Natural Science
ISSN: 10020071
Appears in Collections:Staff Publications

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