Advances in Kernel Methods: Support Vector Learning

By Rosanna Soentpiet

Book Overview

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.ContributorsPeter Bartlett, Kristin P. Bennett, Christopher J.C. Burges, Nello Cristianini, Alex Gammerman, Federico Girosi, Simon Haykin, Thorsten Joachims, Linda Kaufman, Jens Kohlmorgen, Ulrich Kre el, Davide Mattera, Klaus-Robert M ller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar R tsch, Bernhard Sch lkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson

• Format: Hardcover
• Language: English
• ISBN: 0262194163
• ISBN13: 9780262194167
• Release Date: December 1998
• Publisher: Mit Pr
• Length: 376 Pages
• Weight: 2.56 lbs.
• Dimensions: 10.5" x 1.3" x 8.8"