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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, October 24, 2000}
\centerline{Sequoia Hall Rm. 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Trevor Hastie}
\centerline{\sl Department of Statistics}
\centerline{\sl Stanford University}
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\centerline{\bf Support Vector Machines - a Statistics Perspective}
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The SVM techniques pioneered by Vladimir Vapnik has created a growth
industry in computer science and machine learning. Originally developed as
enhancements of the separating hyperplane for two-class classification, we
now have SVM versions of regression, principal components, time-series
models, ..., and the crank is still turning.

In this talk I describe the SVM, and its use of "inner-product" kernels to
achieve flexible generalizations. I then view the SVM as as the
minimization of a regularized error function in a reproducing kernel
Hilbert space of functions, with strong connections to the smoothing spline
technology of Grace Wahba.

This talk is based on material from chapter 11 of "Elements of Statistical
Learning", a monograph near completion by Friedman, Hastie and Tibshirani.
Current versions of this chapter are available from
http://www-stat.stanford.edu/~tibs/book

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