On implicit Lagrangian twin support vector regression by Newton method
In this work, an implicit Lagrangian for the dual twin support vector regression is proposed. Our formulation leads to determining non-parallel –insensitive down- and up- bound functions for the unknown regressor by constructing two unconstrained quadratic programming problems of smaller s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2014-01-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25868471.pdf |
| Summary: | In this work, an implicit Lagrangian for the dual twin support vector regression is proposed. Our formulation leads to determining non-parallel –insensitive down- and up- bound functions for the unknown regressor by constructing two unconstrained quadratic programming problems of smaller size, instead of a single large one as in the standard support vector regression (SVR). The two related support vector machine type problems are solved using Newton method. Numerical experiments were performed on a number of interesting synthetic and real-world benchmark datasets and their results were compared with SVR and twin SVR. Similar or better generalization performance of the proposed method clearly illustrates its effectiveness and applicability. |
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| ISSN: | 1875-6883 |