[PDF.57av] Advances in Kernel Methods: Support Vector Learning

Advances in Kernel Methods: Support Vector Learning

[PDF.tl66] Advances in Kernel Methods: Support Vector Learning

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 | #4338681 in Books |  1998-12-18 | Original language:English | PDF # 1 |  10.00 x1.30 x8.00l, | File type: PDF | 386 pages

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||10 of 11 people found the following review helpful.| a summary of research on support vector machines|By Abstract Space|This is a collection of papers presented at a NIPS workshop held in 1997. So it provides a good entry point for access to forefronts of this rapidly developing field. Many leading researchers have contributed to this volume including V. vapnik who wrote a very succinct and readable survey. The introduction (|About the Author|Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support

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.
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