Journal
NEURAL NETWORKS
Volume 21, Issue 10, Pages 1548-1555Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2008.09.001
Keywords
Support vector regression; Non-convex loss function; Concave-convex procedure
Funding
- National Natural Science Foundation of China [50576033]
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The classical support vector regressions (SVRs) are constructed based on convex loss functions. Since non-convex loss functions to a certain extent own superiority to convex ones in generalization performance and robustness, we propose a non-convex loss function for SVR, and then the concave-convex procedure is utilized to transform the non-convex optimization to convex one. In the following, a Newton-type optimization algorithm is developed to solve the proposed robust SVR in the primal, which can not only retain the sparseness of SVR but also oppress outliers in the training examples. The effectiveness, namely better generalization, is validated through experiments on synthetic and real-world benchmark data sets. (C) 2008 Elsevier Ltd. All rights reserved.
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