book announcement--Smola

[Jud Wolfskill]

I thought readers of the Connectionists List might be interested in
this book.  For table of contents and more information please visit
http://mitpress.mit.edu/promotions/books/SMOAHF00.

Best,

Jud


Advances in Large-Margin Classifiers

edited by Alexander J. Smola, Peter J. Bartlett, Bernhard Schlkopf,
and Dale Schuurmans


The concept of large margins is a unifying principle for the analysis
of many different approaches to the classification of data from
examples, including boosting, mathematical programming, neural
networks, and support vector machines. The fact that it is the margin,
or confidence level, of a classification --that is, a scale
parameter--rather than a raw training error that matters has become a
key tool for dealing with classifiers. This book shows how this idea
applies to both the theoretical analysis and the design of algorithms.



The book provides an overview of recent developments in large margin
classifiers, examines connections with other methods (e.g., Bayesian
inference), and identifies strengths and weaknesses of the method, as
well as directions for future research. Among the contributors are
Manfred Opper, Vladimir Vapnik, and Grace Wahba.



Alexander J. Smola is a Postdoctoral Fellow at GMD-FIRST, Berlin. Peter
L. Bartlett is Senior Fellow, Computer Sciences Laboratory, Australian
National University. Bernhard Schlkopf is a Researcher at Microsoft
Research Ltd., Cambridge, UK. Dale Schuurmans is Assistant Professor of
Computer Science at the University of Waterloo, Ontario, Canada.


8 x 10, 412 pp., cloth ISBN 0-262-19448-1

Neural Information Processing series

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