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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 14, 2003}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Peter Hall}
\centerline{\sl Australian National University}
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\centerline{\bf An Approach to Theory for Classification in High-Dimensional, Low Sample-Size Settings }
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Abstract:

We suggest a way of developing theory describing the
error rates of different classifiers based on space-
splitting planes.  It elucidates both similarities and
differences among classifiers such as those founded
on the support-vector machine, distance-weighted
discrimination, nearest-neighbour methods and the
centroid rule.

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