Support vector machines
IEEE Intelligent Systems and their ApplicationsPublished 1 July 1998
Marti A. Hearst, Susan Dumais, E. Osuna, John Platt, Bernhard Schölkopf
Citations6,931
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TL;DR
This issue's collection of essays should help familiarize readers with this interesting new racehorse in the Machine Learning stable, and give a practical guide and a new technique for implementing the algorithm efficiently.
Abstract
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 computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently.
Keywords
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