Kernel adaptive filtering : a comprehensive introduction / Weifeng Liu; José C. Principe; Simon Haykin

Von:

Liu, Weifeng [aut]

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Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Adaptive and learning systems for signal processing, communication, and control | Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control Ser ; v.57Verlag: Hoboken, N.J : John Wiley, c2010Auflage: Online-AusgBeschreibung: Online-Ressource (1 online resource (xxi, 209 p.)) : illISBN:

9780470447536
9781282549777
1282549774
9780470608586

Schlagwörter:

Andere physische Formen: 1282547526 | 9780470447536 | Erscheint auch als: 1282547526 Druck-Ausgabe | Erscheint auch als: 9780470447536 Druck-AusgabeDDC-Klassifikation:

621.38223
621.382/23

RVK: RVK: ZN 6025LOC-Klassifikation:

TK7872.F5

Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: 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.Zusammenfassung: KERNEL ADAPTIVE FILTERING -- CONTENTS -- PREFACE -- ACKNOWLEDGMENTS -- NOTATION -- ABBREVIATIONS AND SYMBOLS -- 1 BACKGROUND AND PREVIEW -- 1.1 Supervised, Sequential, and Active Learning -- 1.2 Linear Adaptive Filters -- 1.3 Nonlinear Adaptive Filters -- 1.4 Reproducing Kernel Hilbert Spaces -- 1.5 Kernel Adaptive Filters -- 1.6 Summarizing Remarks -- Endnotes -- 2 KERNEL LEAST-MEAN-SQUARE ALGORITHM -- 2.1 Least-Mean-Square Algorithm -- 2.2 Kernel Least-Mean-Square Algorithm -- 2.3 Kernel and Parameter Selection -- 2.4 Step-Size Parameter -- 2.5 Novelty Criterion -- 2.6 Self-Regularization Property of KLMS -- 2.7 Leaky Kernel Least-Mean-Square Algorithm -- 2.8 Normalized Kernel Least-Mean-Square Algorithm -- 2.9 Kernel ADALINE -- 2.10 Resource Allocating Networks -- 2.11 Computer Experiments -- 2.12 Conclusion -- Endnotes -- 3 KERNEL AFFINE PROJECTION ALGORITHMS -- 3.1 Affine Projection Algorithms -- 3.2 Kernel Affine Projection Algorithms -- 3.3 Error Reusing -- 3.4 Sliding Window Gram Matrix Inversion -- 3.5 Taxonomy for Related Algorithms -- 3.6 Computer Experiments -- 3.7 Conclusion -- Endnotes -- 4 KERNEL RECURSIVE LEAST-SQUARES ALGORITHM -- 4.1 Recursive Least-Squares Algorithm -- 4.2 Exponentially Weighted Recursive Least-Squares Algorithm -- 4.3 Kernel Recursive Least-Squares Algorithm -- 4.4 Approximate Linear Dependency -- 4.5 Exponentially Weighted Kernel Recursive Least-Squares Algorithm -- 4.6 Gaussian Processes for Linear Regression -- 4.7 Gaussian Processes for Nonlinear Regression -- 4.8 Bayesian Model Selection -- 4.9 Computer Experiments -- 4.10 Conclusion -- Endnotes -- 5 EXTENDED KERNEL RECURSIVE LEAST-SQUARES ALGORITHM -- 5.1 Extended Recursive Least Squares Algorithm -- 5.2 Exponentially Weighted Extended Recursive Least Squares Algorithm -- 5.3 Extended Kernel Recursive Least Squares Algorithm.PPN: PPN: 808848119Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PQE

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