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 workshopon Support Vector Machines held at the 1997 NIPS conference The contributors, both university оеэян 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 Kreel, Davide Mattera, Klaus-Robert Mller, Manfred Opper, Edgar E Osuna, John C Platt, Gunnar Rtsch, Bernhard Schlkopf, John Shawe-Taylor, Alexander J Smola, Mark O Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C Williamson.  PERFECTISBN 0262194163.