An Interior Point Method for L1/2-SVM and Application to Feature Selection in Classification
This paper studies feature selection for support vector machine (SVM). By the use of the L1/2 regularization technique, we propose a new model L1/2-SVM. To solve this nonconvex and non-Lipschitz optimization problem, we first transform it into an equivalent quadratic constrained optimization model w...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/942520 |