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Synopsis
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
Bernhard Schölkopf is on the technical staff of Microsoft Research Limited in Cambridge, England. Alexander J. Smola is a research scientist at GMD FIRST, in Berlin, Germany. Christopher J. C. Burges is Distinguished Member of Technical Staff at Lucent Technologies, Bell Laboratories.
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Advances in Kernel Methods: Support Vector Learning
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The MIT Press, Cambridge , Mass, 1998
ISBN 10: 0262194163
ISBN 13: 9780262194167
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