Support Vector Machines for Regression: A Succinct Review of Large-Scale and Linear Programming Formulations

Efficient Large Scale Linear Programming Support Vector Machines

This paper presents a decomposition method for efficiently constructing 1-norm Support Vector Machines (SVMs). The decomposition algorithm introduced in this paper possesses many desirable properties. For example, it is provably convergent, scales well to large datasets, is easy to implement, and can be extended to handle support vector regression and other SVM variants. We demonstrate the effi...

Large-scale linear support vector regression

Support vector regression (SVR) and support vector classification (SVC) are popular learning techniques, but their use with kernels is often time consuming. Recently, linear SVC without kernels has been shown to give competitive accuracy for some applications, but enjoys much faster training/testing. However, few studies have focused on linear SVR. In this paper, we extend state-of-the-art trai...

Support Vector Machines for Large-Scale Regression Problems

SVMTorch: Support Vector Machines for Large-Scale Regression Problems

Support Vector Machines (SVMs) for regression problems are trained by solving a quadratic optimization problem which needs on the order of l memory and time resources to solve, where l is the number of training examples. In this paper, we propose a decomposition algorithm, SVMTorch, which is similar to SVM-Light proposed by Joachims (1999) for classi cation problems, but adapted to regression p...

Linear programming support vector machines

Based on the analysis of the conclusions in the statistical learning theory, especially the VC dimension of linear functions, linear programming support vector machines (or SVMs) are presented including linear programming linear and nonlinear SVMs. In linear programming SVMs, in order to improve the speed of the training time, the bound of the VC dimension is loosened properly. Simulation resul...