An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods

This paper presents a method to train a Support Vector Regression (SVR) model for the large-scale case where the number of training samples supersedes the computational resources. The proposed scheme consists of posing the SVR problem entirely as a Linear Programming (LP) problem and on the development of a sequential optimization method based on variables decomposition, constraints decomposition, and the use of primal–dual interior point methods. Experimental results demonstrate that the proposed approach has comparable performance with other SV-based classifiers. Particularly, experiments demonstrate that as the problem size increases, the sparser the solution becomes, and more computational efficiency can be gained in comparison with other methods. This demonstrates that the proposed learning scheme and the LP-SVR model are robust and efficient when compared with other methodologies for large-scale problems.

► We address the problem of large-scale non-linear regression and classification.
► We overcome the natural limitation of large-scale problems particular to SVR.
► We propose a sequential linear programming Support Vector Regression approach.
► The large-scale training algorithm has fast rate of convergence.