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Ramp loss nonparallel support vector machine for pattern classification
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DOI：10.1016/j.knosys.2015.05.008.png)
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Abstract
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In this paper, we propose a novel sparse and robust nonparallel hyperplane classifier, named Ramp loss Nonparallel Support Vector Machine (RNPSVM), for binary classification. By introducing the Ramp loss function and also proposing a new non-convex and non-differentiable loss function based on the epsilon-insensitive loss function, RNPSVM can explicitly incorporate noise and outlier suppression in the training process, has less support vectors and the increased sparsity leads to its better scaling properties. The non-convexity of RNPSVM can be efficiently solved by the Concave-Convex Procedure and experimental results on benchmark datasets confirm the effectiveness of the proposed algorithm. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Support vector machine
Twin support vector machine
CCCP
Ramp loss
Sparseness
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