Advances in Kernel Methods: Support Vector Learning - Rilegato

Rilegato

ISBN 10: 0262194163 ISBN 13: 9780262194167

Casa editrice: Mit Pr, 1998

Riassunto

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.

Su questa edizione

• Editore: Mit Pr
• Data di pubblicazione: 1998
• ISBN 10: 0262194163
• ISBN 13: 9780262194167
• Rilegatura: Copertina rigida
• Numero di pagine: 376
• Redattore: Scholkopf Bernhard, Burges Christopher J. C., Smola Alexander J.