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SVMs — a practical consequence of learning theory




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.

Author(s): Schölkopf, B.
Journal: IEEE Intelligent Systems and their Applications
Volume: 13
Number (issue): 4
Pages: 18-21
Year: 1998
Month: July
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1109/5254.708428

Links: PDF


  title = {SVMs — a practical consequence of learning theory},
  author = {Sch{\"o}lkopf, B.},
  journal = {IEEE Intelligent Systems and their Applications},
  volume = {13},
  number = {4},
  pages = {18-21},
  month = jul,
  year = {1998},
  month_numeric = {7}