Support Vector Workshop

[Bernhard Schoelkopf]

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  Call for Contributions
  NIPS'97 Workshop on
  Support Vector Learning Machines
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The Support Vector (SV) learning algorithm (Boser, Guyon, Vapnik, 1992;
Cortes, Vapnik, 1995; Vapnik, 1995) provides a general method for solving
Pattern Recognition, Regression Estimation and Operator Inversion problems.
The method is based on results in the theory of learning with finite sample
sizes. The last few years have witnessed an increasing interest in SV
machines, due largely to excellent results in pattern recognition, regression
estimation and time series prediction experiments.

The purpose of this workshop is (1) to provide an overview of recent
developments in SV machines, ranging from theoretical results to applications,
(2) to explore connections with other methods, and (3) to identify weaknesses,
strengths and directions for future research for SVMs.  We invite
contributions on SV machines and related approaches, looking for empirical
support wherever possible. Topics of interest to the workshop include:

  SV Applications
  Benchmarks
  SV Optimization and implementation issues
  Theory of generalization and regularization
  Learning methods based on Hilbert-Schmidt kernels (e.g. kernel PCA)
  Links to related methods and concepts (e.g. boosting, fat shattering)
  Representation of functions in SV machines (e.g. splines, anova)


The workshop will be held in Breckenridge, Colorado, on December 5 or 6, 1997.
We are in the process of putting together a tentative schedule.  If you are
interested in contributing, please contact us (mailto:smola at first.gmd.de, or
faxto:+49-30-6392-1805, A. Smola). Submission of papers is not required for
the workshop. We would, however, appreciate a brief description of your
envisaged talk. As one of the workshop's foci will be discussions,
presentation of recent and/or controversial work is encouraged.

A workshop home page has been set up at http://svm.first.gmd.de.
Additional information on SV research can be found at
http://svm.research.bell-labs.com/ (Bell Labs SV services) and
http://www.mpik-tueb.mpg.de/people/personal/bs/svm.html (annotated SV
bibliography).


  Organizers:

Leon Bottou (AT&T Research, leonb at research.att.com)
Chris Burges (Bell Labs, Lucent Technologies, burges at bell-labs.com)
Bernhard Schoelkopf (Max Planck Institute at Tuebingen, bs at mpik-tueb.mpg.de)
Alex Smola (Technical University/GMD Berlin, smola at first.gmd.de)


-- 

 bernhard schoelkopf
 mailto:bs at mpik-tueb.mpg.de
 max-planck-institut fuer biologische kybernetik
 spemannstr.38, 72076 tuebingen, germany
 phone +49 7071 601-609, fax -616
 http://www.mpik-tueb.mpg.de/people/personal/bs/bs.html