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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big STATISTICS SEMINAR}
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\centerline{4:15 p.m., Tuesday, April 27, 2004}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in the 1st Floor Lounge)}


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\centerline{\sl Trevor Hastie}
\centerline{\sl Stanford University}
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\centerline{\bf The Entire Regularization Path for the Support Vector Machine}
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Abstract:

  The Support Vector Machine is a widely used tool for classification.
  Many efficient implementations exist for fitting a two-class SVM
  model. The user has to supply values for the tuning parameters: the
  regularization cost parameter, and the kernel parameters. It seems a
  common practice is to use a default value for the cost parameter,
  often leading to the least restrictive model. In this paper we argue
  that the choice of the cost parameter can be critical. We then
  derive an algorithm that can fit the entire path of SVM solutions
  for every value of the cost parameter, with essentially the same
  computational cost as fitting one SVM model. We illustrate our
  algorithm on some examples, and use our representation to give
  further insight into the range of SVM solutions.
 
  This is joint work with Saharon Rosset, Rob Tibshirani, and Ji Zhu.




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