Advances in Kernel Methods: Support Vector Learning
Editorial Reviews
Book Description
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 Müller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Rätsch, Bernhard Schölkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson.
Book Info
Provides a collection of papers submitted during a workshop at the annual Neural Information Processing Systems (NIPS) conference, held in Breckenridge, Colorado in December 1997. DLC: Machine learning.
Advances in Kernel Methods: Support Vector Learning
Advances in Kernel Methods: Support Vector Learning,Bernhard Schölkopf,Christopher J. C. Burges,Alexander J. Smola,The MIT Press,0262194163,Algorithms,Algorithms (Computer Programming),Artificial Intelligence - General,Computer Books: General,Computers,Computers - General Information,Kernel functions,Machine Learning,Neural Networks,Computers / Neural Networks
Discount Books:
Recommended Books