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Kernel Adaptive Filtering

Kernel Adaptive Filtering

Weifeng Liu
5/5 ( ratings)
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Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters.

Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm

Presents a powerful model-selection method called maximum marginal likelihood

Addresses the principal bottleneck of kernel adaptive filters--their growing structure

Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site

Concludes each chapter with a summary of the state of the art and potential future directions for original research

Kernel Adaptive Filtering is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications . It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems.
Language
English
Pages
240
Format
Hardcover
Publisher
Wiley
Release
March 01, 2010
ISBN
0470447532
ISBN 13
9780470447536

Kernel Adaptive Filtering

Weifeng Liu
5/5 ( ratings)
Read Download
Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters.

Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm

Presents a powerful model-selection method called maximum marginal likelihood

Addresses the principal bottleneck of kernel adaptive filters--their growing structure

Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site

Concludes each chapter with a summary of the state of the art and potential future directions for original research

Kernel Adaptive Filtering is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications . It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems.
Language
English
Pages
240
Format
Hardcover
Publisher
Wiley
Release
March 01, 2010
ISBN
0470447532
ISBN 13
9780470447536

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