Advances in Kernel Methods: Support Vector Learning

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Philomel Books, 1999 - Computers - 376 pages
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The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.

Contributors: Peter Bartlett, Kristin P. Bennett, Christopher J. C. Burges, Nello Cristianini, Alex Gammerman, Federico Girosi, Simon Haykin, Thorsten Joachims, Linda Kaufman, Jens Kohlmorgen, Ulrich Kressel, Davide Mattera, Klaus-Robert Muller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Ratsch, Bernhard Scholkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson.

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About the author (1999)

Bernhard SchAlkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in TA1/4bingen, Germany. He is coauthor of "Learning with Kernels" (2002) and is a coeditor of "Advances in Kernel Methods: Support Vector Learning" (1998), "Advances in Large-Margin Classifiers" (2000), and "Kernel Methods in Computational Biology" (2004), all published by the MIT Press.

Burges is Distinguished Member of Technical Staff at Lucent Technologies, Bell Laboratories.

Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

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