Advances in Kernel Methods: Support Vector Learning

ISBN 10: 0262194163 / ISBN 13: 9780262194167

Published by Mit Pr, 1998

About this Item

Fast Shipping - Good and clean conditions used book. Pages and cover are intact. Limited notes marks and highlighting may be present. May show signs of normal shelf wear and bends on edges. Item may be missing CDs or access codes. May include library marks.

Seller Inventory # ZWV.0262194163.G

Bibliographic Details

• Title: Advances in Kernel Methods: Support Vector Learning
• Publisher: Mit Pr
• Publication Date: 1998
• Language: English
• ISBN 10: 0262194163
• ISBN 13: 9780262194167
• Binding: Hardcover
• Condition: good

About this title

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.

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.