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Advances in Kernel Methods: Support Vector Learning

Автор: Bernhard Schlkopf, Christopher J. C. Burges, Alexander J. Smola
Год: [не указано]
Издание: [не указанo]
Страниц: [не указано]
ISBN: 0262194163
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.
Добавлено: 2013-10-22 15:56:21

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