Kernel Adaptive Filtering A Comprehensive

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.

Weifeng Liu, PhD, is a senior engineer of the Demand Forecasting Team at Amazon.com Inc. His research interests include kernel adaptive filtering, online active learning, and solving real-life large-scale data mining problems. Jos C. Prncipe is Distinguished Professor of Electrical and Biomedical Engineering at the University of Florida, Gainesville, where he teaches advanced signal processing and artificial neural networks modeling. He is BellSouth Professor and founder and Director of the University of Florida Computational Neuro-Engineering Laboratory. Simon Haykin is Distinguished University Professor at McMaster University, Canada. He is world-renowned for his contributions to adaptive filtering applied to radar and communications. Haykin's current research passion is focused on cognitive dynamic systems, including applications on cognitive radio and cognitive radar.

Preface	p. xi
Acknowledgments	p. xv
Notation	p. xvii
Abbreviations and Symbols	p. xix
Background and Preview	p. 1
Supervised, Sequential, and Active Learning	p. 1
Linear Adaptive Filters	p. 3
Nonlinear Adaptive Filters	p. 10
Reproducing Kernel Hilbert Spaces	p. 12
Kernel Adaptive Filters	p. 16
Summarizing Remarks	p. 20
Endnotes	p. 21
Kernel Least-Mean-Square Algorithm	p. 27
Least-Mean-Square Algorithm	p. 28
Kernel Least-Mean-Square Algorithm	p. 31
Kernel and Parameter Selection	p. 34
Step-Size Parameter	p. 37
Novelty Criterion	p. 38
Self-Regularization Property of KLMS	p. 40
Leaky Kernel Least-Mean-Square Algorithm	p. 48
Normalized Kernel Least-Mean-Square Algorithm	p. 48
Kernel ADALINE	p. 49
Resource Allocating Networks	p. 53
Computer Experiments	p. 55
Conclusion	p. 63
Endnotes	p. 65
Kernel Affine Projection Algorithms	p. 69
Affine Projection Algorithms	p. 70
Kernel Affine Projection Algorithms	p. 72
Error Reusing	p. 77
Sliding Window Gram Matrix Inversion	p. 78
Taxonomy for Related Algorithms	p. 78
Computer Experiments	p. 80
Conclusion	p. 89
Endnotes	p. 91
Kernel Recursive Least-Squares Algorithm	p. 94
Recursive Least-Squares Algorithm	p. 94
Exponentially Weighted Recursive Least-Squares Algorithm	p. 97
Kernel Recursive Least-Squares Algorithm	p. 98
Approximate Linear Dependency	p. 102
Exponentially Weighted Kernel Recursive Least-Squares Algorithm	p. 103
Gaussian Processes for Linear Regression	p. 105
Gaussian Processes for Nonlinear Regression	p. 108
Bayesian Model Selection	p. 111
Computer Experiments	p. 114
Conclusion	p. 119
Endnotes	p. 120
Extended Kernel Recursive Least-Squares Algorithm	p. 124
Extended Recursive Least Squares Algorithm	p. 125
Exponentially Weighted Extended Recursive Least Squares Algorithm	p. 128
Extended Kernel Recursive Least Squares Algorithm	p. 129
EX-KRLS for Tracking Models	p. 131
EX-KRLS with Finite Rank Assumption	p. 137
Computer Experiments	p. 141
Conclusion	p. 150
Endnotes	p. 151
Definition of Surprise	p. 152
A Review of Gaussian Process Regression	p. 154
Computing Surprise	p. 156
Kernel Recursive Least Squares with Surprise Criterion	p. 159
Kernel Least Mean Square with Surprise Criterion	p. 160
Kernel Affine Projection Algorithms with Surprise Criterion	p. 161
Computer Experiments	p. 162
Conclusion	p. 173
Endnotes	p. 174
Epilogue	p. 175
Appendix	p. 177
Mathematical Background	p. 177
Singular Value Decomposition	p. 177
Positive-Definite Matrix	p. 179
Eigenvalue Decomposition	p. 179
Schur Complement	p. 181
Block Matrix Inverse	p. 181
Matrix Inversion Lemma	p. 182
Joint, Marginal, and Conditional Probability	p. 182
Normal Distribution	p. 183
Gradient Descent	p. 184
Newton's Method	p. 184
Approximate Linear Dependency and System Stability	p. 186
References	p. 193
Index	p. 204
Table of Contents provided by Ingram. All Rights Reserved.

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Kernel Adaptive Filtering A Comprehensive Introduction

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