Support vector machines
Hearst, M.A.; Dumais, S.T.; Osman, E.; Platt, J.; Scholkopf, B.
Intelligent Systems and their Applications, IEEE
Volume 13, Issue 4, Jul/Aug 1998 Page(s):18 - 28
Digital Object Identifier 10.1109/5254.708428
Summary: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
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