Обложка книги Advances in Kernel Methods: Support Vector Learning

Advances in Kernel Methods: Support Vector Learning

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ISBN: 0262194163; 9780262194167;
Издательство: The MIT Press
Страниц: 376

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 workshopon 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 Kreel, Davide Mattera, Klaus-Robert Mller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Rtsch, Bernhard Schlkopf, 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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