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Error tolerance based support vector machine for regression
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DOI：10.1016/j.neucom.2010.10.002.png)
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Abstract
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Most existing online algorithms in support vector machines (SVM) can only grow support vectors. This paper proposes an online error tolerance based support vector machine (ET-SVM) which not only grows but also prunes support vectors. Similar to least square support vector machines (LS-SVM), ET-SVM converts the original quadratic program (QP) in standard SVM into a group of easily solved linear equations. Different from LS-SVM, ET-SVM remains support vectors sparse and realizes a compact structure. Thus, ET-SVM can significantly reduce computational time while ensuring satisfactory learning accuracy. Simulation results verify the effectiveness of the newly proposed algorithm. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Support vector machine (SVM)
Error tolerance based support vector machine (ET-SVM)
Online learning
Growing and pruning support vectors
Fast algorithm
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