Advances in Kernel Methods: Support Vector Learning
Bernhard Schölkopf, Christopher J. C. Burges, Alexander J. Smola
MIT Press, 1999 - Computers - 376 pages
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.
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Using Support Vector Machines for Time Series Prediction
Pairwise Classification and Support Vector Machines
Reducing the Runtime Complexity in Support Vector Machines
Support Vector Regression with ANOVA Decomposition Kernels
Support Vector Density Estimation
Combining Support Vector and Mathematical Programming
Kernel Principal Component Analysis
Making LargeScale Support Vector Machine Learning Practical
Fast Training of Support Vector Machines Using Sequential
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Advances in kernel methods : support vector learning [worldcat.org]
Advances in kernel methods : support vector learning. By: Bernhard Schölkopf; Christopher jc Burges; Alexander J Smola. Type: English : Book ...
worldcat.org/ isbn/ 9780262194167
Advances in Kernel Methods - Support Vector Learning - Sch, Burges ...
This paper should not be used as an indication of the quality of the method. The primary weakness of the MPM approaches is that they have not been guided by ...
Advances in Kernel Methods
1998/08/25 16:31. Advances in Kernel Methods. Support Vector Learning. edited by. Bernhard Scholkopf. Christopher jc Burges. Alexander J. Smola ...
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New Support Vector Algorithms -- Schölkopf et al. 12 (5): 1207 ...
This Article. Right arrow, Abstract Freely available. Right arrow, Full Text (PDF). Right arrow, Alert me when this article is cited. Right arrow ...
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Advances in kernel methods
Advances in kernel methods: support vector learning. Purchase this Divisible Book · Purchase this Divisible Book. Source. Advances in kernel methods table ...
Books: Advances in Kernel Methods
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A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik ...
 T. Joachims, Making Large-Scale SVM Learning Practical Advances in Kernel Methods Support Vector Learning, B. Scholkopf, cjc Burges, and aj Smola, ...
csdl.computer.org/ comp/ trans/ tk/ 2004/ 04/ k0385abs.htm
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Resources on the WWW. The following is a list of pointers to websites with useful Kernel Method related resources:. General Sites on Kernel Methods and svms ...
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year); JOACHIMS, T., 1999. Making large-scale support vector machine learning practical. Advances in kernel methods: support vector learning table of … ...
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An extrapolated Sequential Minimal Optimization Algorithm for. Support Vector Machines. *D.Lai. ,. *N.Mani, +M.Palaniswami ...
ieeexplore.ieee.org/ iel5/ 9048/ 28701/ 01287693.pdf?tp=&