Advances in Kernel Methods

Support Vector Learning

• Author(s): Bernhard Schölkopf, Christopher J. C. Burges, Alexander J. Smola,
• Publisher: MIT Press
• Pages: 376
• ISBN_10: 0262194163
  ISBN_13: 9780262194167
  
• Language: en
• Categories: Computers / Artificial Intelligence / General , Computers / Computer Science , Computers / Data Science / Neural Networks , Computers / Data Science / Machine Learning , Mathematics / Algebra / General , Mathematics / Vector Analysis , Psychology / General ,

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.ContributorsPeter 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