On implicit Lagrangian twin support vector regression by Newton method

DOI

10.1080/18756891.2013.869900How to use a DOI?

Keywords

Implicit Lagrangian support vector machines, Non parallel planes, Support vector regression, Twin support vector regression

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - S. Balasundaram
AU  - Deepak Gupta
PY  - 2014
DA  - 2014/02/03
TI  - On implicit Lagrangian twin support vector regression by Newton method
JO  - International Journal of Computational Intelligence Systems
SP  - 50
EP  - 64
VL  - 7
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.869900
DO  - 10.1080/18756891.2013.869900
ID  - Balasundaram2014
ER  -