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A flexible support vector machine for regression.
- Source :
-
Neural Computing & Applications . Nov2012, Vol. 21 Issue 8, p2005-2013. 9p. 2 Diagrams, 3 Charts, 3 Graphs. - Publication Year :
- 2012
-
Abstract
- In this paper, a novel regression algorithm coined flexible support vector regression is proposed. We first model the insensitive zone in classic support vector regression, respectively, by its up- and down-bound functions and then give a kind of generalized parametric insensitive loss function (GPILF). Subsequently, based on GPILF, we propose an optimization criterion such that the unknown regressor and its up- and down-bound functions can be found simultaneously by solving a single quadratic programming problem. Experimental results on both several publicly available benchmark data sets and time series prediction show the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09410643
- Volume :
- 21
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- Neural Computing & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 82503449
- Full Text :
- https://doi.org/10.1007/s00521-011-0623-5