Proceedings ArticleDOI
Benders decomposition technique for support vector regression
Theodore B. Trafalis,Huseyin Ince +1 more
- Vol. 3, pp 2767-2772
TLDR
This work proposes to apply the Benders decomposition technique to the resulting LP for the regression case, and preliminary results show that this technique is much faster than the QP formulation.Abstract:
The theory of the support vector machine (SVM) algorithm is based on the statistical learning theory. Training of SVMs leads to either a quadratic programming (QP) problem, or linear programming (LP) problem. This depends on the specific norm that is used when the distance between the convex hulls of two classes are computed. The l/sub 1/ norm distance leads to a large scale linear programming problem in the case where the sample size is very large. We propose to apply the Benders decomposition technique to the resulting LP for the regression case. Preliminary results show that this technique is much faster than the QP formulation.read more
Citations
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Journal ArticleDOI
Short term forecasting with support vector machines and application to stock price prediction
TL;DR: Comparison shows that support vector regression (SVR) out performs the multi layer perceptron (MLP) networks for a short term prediction in terms of the mean square error and if the risk premium is used as a comparison criterion, then the SVR technique is as good as the MLP method or better.
Proceedings ArticleDOI
Kernel principal component analysis and support vector machines for stock price prediction
TL;DR: This work assumes that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship, and comparison shows that SVR and MLP networks require different inputs.
Journal ArticleDOI
Kernel principal component analysis and support vector machines for stock price prediction
TL;DR: There is no difference between MLP networks and SVR techniques when the authors compare their mean square error values, and proposed heuristic models produce better results than the studied data mining methods.
Journal ArticleDOI
Feature selection for support vector machines using Generalized Benders Decomposition
TL;DR: An exact method, based on Generalized Benders Decomposition, to select the best M features during induction and a relaxation of the problem where selecting too many features is penalized is proposed.
References
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A Tutorial on Support Vector Machines for Pattern Recognition
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Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
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