Main Advances in Kernel Methods: Support Vector Learning
Advances in Kernel Methods: Support Vector Learning cover

Advances in Kernel Methods: Support Vector Learning

, ,
5.0 / 5.0
0 comments
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.
Request Code : ZLIBIO4285513
Categories:
Year:
1998
Publisher:
Mit Pr
Language:
English
Pages:
376
ISBN 10:
0262194163
ISBN 13:
9780262194167
ISBN:
0262194163,9780262194167

Comments of this book

There are no comments yet.