Support vector machines (SVMs) are an extremely successful class of classification and regression algorithms. Building an SVM entails the solution of a constrained convex quadratic programming problem which is quadratic in the number of training samples. Previous parallel implementations of SVM solvers sequentially solved subsets of the complete problem, which is problematic when the solution r...

Multivariate data analysis techniques have the potential to improve data analysis. Support Vector Machines (SVS) are a recent addition to the family of multivariate data analysis. A brief introduction to the SVM Vector Machines technique is followed by an outline of the practical application Key-Words: SVM vector machines, data analysis

We examine the problem faced by owners of gas storage contracts of how to manage the injection/withdrawal schedule of gas, given historical price behavior and a predictive model of future prices. We describe a software application, deployed with a gas utility, that involves a hybrid price prediction model that combines a support vector machine technique with a stochastic model for short and lon...

High Dimension Low Sample Size statistical analysis is becoming increasingly important in a wide range of applied contexts. In such situations, it is seen that the appealing discrimination method called the Support Vector Machine can be improved. The revealing concept is “data piling” at the margin. This leads naturally to the development of “Distance Weighted Discrimination,” which also is bas...

Multivariate data analysis techniques have the potential to improve physics analyses in many ways. The common classification problem of signal/background discrimination is one example. A comparison of a conventional method and a Support Vector Machine algorithm is presented here for the case of identifying top quark signal events in the dilepton decay channel amidst a large number of background...

We show that the orientation and location of the separating hyperplane for 2-class supervised pattern classi cation obtained by the Support Vector Machine (SVM) proposed by Vapnik and his colleagues, is equivalent to the solution obtained by Fisher's Linear Discriminant on the set of Support Vectors. In other words, SVM can be seen as a way to \sparsify" Fisher's Linear Discriminant in order to...

This paper presents an automatic video based face verification and recognition system by Support Vector Machines (SVMs). Faces as training samples are automatically extracted from input video sequences in real-time by LUT-based Adaboost and are normalized both in geometry and in gray level distribution after facial landmark localization via Simple Direct Appearance Model (SDAM). Two different s...