Band 1
Kernel Adaptive Filtering A Comprehensive Introduction
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Form:Einzelkauf Download
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Sprache:Englisch
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eBook Format:ePUB
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Produktdetails
Format
ePUB
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
20.09.2011
Verlag
John Wiley & Sons IncSeitenzahl
240 (Printausgabe)
Dateigröße
18905 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9781118211212
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 (applications where the
data stream arrives one sample at a time and incremental optimal
solutions are desirable). It is also a useful guide for those who
look for nonlinear adaptive filtering methodologies to solve
practical problems.
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 (applications where the
data stream arrives one sample at a time and incremental optimal
solutions are desirable). It is also a useful guide for those who
look for nonlinear adaptive filtering methodologies to solve
practical problems.
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