Advertisement

Support Vector Machines

  • Alan Julian Izenman
Part of the Springer Texts in Statistics book series (STS)

Abstract

Fisher’s linear discriminant function (LDF) and related classifiers for binary and multiclass learning problems have performed well for many years and for many data sets. Recently, a brand-new learning methodology, support vector machines (SVMs), has emerged (Boser, Guyon, and Vapnik, 1992), which has matched the performance of the LDF and, in many instances, has proved to be superior to it.

Keywords

Support Vector Machine Support Vector Slack Variable Linear Support Vector Machine Sequential Minimal Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Alan Julian Izenman
    • 1
  1. 1.Department of StatisticsTemple UniversityPhiladelphiaUSA

Personalised recommendations