Learning with Kernels
Support Vector Machines, Regularization, Optimization, and Beyond
$147.10
- Paperback
648 pages
- Release Date
5 June 2018
Summary
A comprehensive introduction to Support Vector Machines and related kernel methods.In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory- the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs–kernels-for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the ker…
Book Details
| ISBN-13: | 9780262536578 |
|---|---|
| ISBN-10: | 0262536579 |
| Author: | Alexander J. Smola, Bernhard Schölkopf |
| Publisher: | MIT Press Ltd |
| Imprint: | MIT Press |
| Format: | Paperback |
| Number of Pages: | 648 |
| Release Date: | 5 June 2018 |
| Weight: | 1.29kg |
| Dimensions: | 254mm x 203mm x 27mm |
| Series: | Adaptive Computation and Machine Learning series |
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About The Author
Alexander J. Smola
Bernhard Sch lkopf is Director at the Max Planck Institute for Intelligent Systems in T bingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods- Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.
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