@Article{scholkopf98a,
  author =	 {Bernhard Scholkopf},
  title =	 {Support Vector Machines: A Practical Consequence of
                  Learning Theory},
  googleid =	 {uXZoh3C53IUJ:scholar.google.com/},
  journal =	 {{IEEE} Intelligent Systems},
  year =	 1998,
  volume =	 13,
  number =	 4,
  month =	 {July/August},
  abstract =	 {My first exposure to Support Vector Machines came
                  this spring when I 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 Schölkopf, 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 =     {learning},
  url =		 {http://jmvidal.cse.sc.edu/library/scholkopf98a.pdf},
  doi =		 {10.1041/X4018s-1998},
  cluster = 	 {3511036705251019583}
}