Trends & Controversies: Support Vector Machines

  title={Trends \& Controversies: Support Vector Machines},
  author={Marti A. Hearst},
  journal={IEEE Intell. Syst.},
My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from… 

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An improved training algorithm for support vector machines

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This paper presents a decomposition algorithm that is guaranteed to solve the QP problem and that does not make assumptions on the expected number of support vectors.