Kunst in der Bibliothek
Zur Geschichte der Kunstbibliothek und ihrer Sammlungen Staatliche Museen zu Berlin, Preußischer Kulturbesitz
Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series;
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21 498 Ft (20 475 Ft + 5% VAT)
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21 498 Ft
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Product details:
- Publisher Wiley-Vch
- Date of Publication 14 July 1994
- ISBN 9783050026497
- Binding Hardback
- No. of pages568 pages
- Size 110x92x17 mm
- Weight 2517 g
- Language German 0
Categories
Short description:
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.
MoreLong description:
Reproducing kernel Hilbert spaces is a topic of great current interest for applications in signal processing, communications, and controls The first book to explain real-time learning algorithms in reproducing kernel Hilbert spaces, On-Line Kernel Learning includes simulations that illustrate the ideas discussed and demonstrate their applicability as well as MATLAB codes for simulations. This book is ideal for professionals and graduate students interested in nonlinear adaptive systems for on-line applications.
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.
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