Kernel adaptive filtering a comprehensive introduction
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.
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| Hauptverfasser: | , , |
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| Format: | E-Book Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, N.J
Wiley
2010
WILEY John Wiley & Sons, Incorporated Wiley-Blackwell John Wiley & Sons |
| Ausgabe: | 1 |
| Schriftenreihe: | Adaptive and learning systems for signal processing, communication, and control |
| Schlagworte: | |
| ISBN: | 9780470447536, 0470447532, 0470608587, 9780470608586, 9780470608593, 0470608595 |
| Online-Zugang: | Volltext |
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| Abstract | 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. |
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| AbstractList | 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. '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. |
| Author | Liu, Weifeng Haykin, Simon S Príncipe, J. C |
| Author_xml | – sequence: 1 fullname: Príncipe, J. C – sequence: 2 fullname: Liu, Weifeng – sequence: 3 fullname: Haykin, Simon S |
| BackLink | https://cir.nii.ac.jp/crid/1130000796777661440$$DView record in CiNii |
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| DOI | 10.1002/9780470608593 |
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| EISBN | 0470608587 9780470608586 |
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| Notes | Includes bibliographical references and index Available also in a print ed. Mode of access: Internet via World Wide Web. Title from title screen. |
| OCLC | 368051917 587390074 |
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| PageCount | 209 |
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| PublicationDecade | 2010 |
| PublicationPlace | Hoboken, N.J |
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| PublicationSeriesTitle | Adaptive and learning systems for signal processing, communication, and control |
| PublicationYear | 2010 |
| Publisher | Wiley WILEY John Wiley & Sons, Incorporated Wiley-Blackwell John Wiley & Sons |
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| Snippet | Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for... 'On-Line Kernel Learning' includes simulations that illustrate the ideas discussed and demonstrate their applicability as well as MATLAB codes for simulations.... |
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| SubjectTerms | Adaptive filters Kernel functions Science Waves & Wave Mechanics |
| SubjectTermsDisplay | Adaptive filters. Electronic books. Kernel functions. Science Waves & Wave Mechanics |
| Subtitle | a comprehensive introduction |
| TableOfContents | Kernel adaptive filtering : a comprehensive introduction -- CONTENTS -- PREFACE -- ACKNOWLEDGMENTS -- NOTATION -- ABBREVIATIONS AND SYMBOLS -- 1. BACKGROUND AND PREVIEW -- 2. KERNEL LEAST-MEAN-SQUARE ALGORITHM -- 3. KERNEL AFFINE PROJECTION ALGORITHMS -- 4. KERNEL RECURSIVE LEAST-SQUARES ALGORITHM -- 5. EXTENDED KERNEL RECURSIVE LEAST-SQUARES ALGORITHM -- 6. DESIGNING SPARSE KERNEL ADAPTIVE FILTERS -- EPILOGUE -- APPENDIX A: MATHEMATICAL BACKGROUND -- APPENDIX B: APPROXIMATE LINEAR DEPENDENCY AND SYSTEM STABILITY -- REFERENCES -- INDEX 5.4 EX-KRLS for Tracking Models -- 5.5 EX-KRLS with Finite Rank Assumption -- 5.6 Computer Experiments -- 5.7 Conclusion -- Endnotes -- 6 DESIGNING SPARSE KERNEL ADAPTIVE FILTERS -- 6.1 Definition of Surprise -- 6.2 A Review of Gaussian Process Regression -- 6.3 Computing Surprise -- 6.4 Kernel Recursive Least Squares with Surprise Criterion -- 6.5 Kernel Least Mean Square with Surprise Criterion -- 6.6 Kernel Affine Projection Algorithms with Surprise Criterion -- 6.7 Computer Experiments -- 6.8 Conclusion -- Endnotes -- EPILOGUE -- APPENDIX -- A MATHEMATICAL BACKGROUND -- A.1 Singular Value Decomposition -- A.2 Positive-Definite Matrix -- A.3 Eigenvalue Decomposition -- A.4 Schur Complement -- A.5 Block Matrix Inverse -- A.6 Matrix Inversion Lemma -- A.7 Joint, Marginal, and Conditional Probability -- A.8 Normal Distribution -- A.9 Gradient Descent -- A.10 Newton's Method -- B APPROXIMATE LINEAR DEPENDENCY AND SYSTEM STABILITY -- REFERENCES -- INDEX Intro -- 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 |
| Title | Kernel adaptive filtering |
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