Fast training of Support Vector Machines with Gaussian kernel

Fast training of Support Vector Machines with Gaussian kernel

Support Vector Machines (SVM’s) are ubiquitous and attracted a huge interest in the last years. Their training involves the definition of a suitable optimization model with two main features: (1) its optimal solution estimates the a-posteriori optimal SVM parameters in a reliable way, and (2) it can be solved efficiently. Hinge-loss models, among others, have been used with remarkable success t...

Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyperparameters: the penalty parameter C and the kernel width sigma. This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values. Our results help in understanding the hyperparameter spac...

Proposed Gaussian Kernel Support Vector Machines for Regression

A fast algorithm for kernel 1-norm support vector machines

This paper presents a fast algorithm called Column Generation Newton (CGN) for kernel 1-norm support vector machines (SVMs). CGN combines the Column Generation (CG) algorithm and the Newton Linear Programming SVM (NLPSVM) method. NLPSVM was proposed for solving 1-norm SVM, and CG is frequently used in large-scale integer and linear programming algorithms. In each iteration of the kernel 1-norm ...

An Efficient Training Algorithm for Kernel Survival Support Vector Machines

Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support. In order to leverage large amounts of patient data, efficient optimisation routines are paramount. We propose an efficient training algorithm for the kernel survival support vector machine (SSVM). We directly optimise the primal objective functi...