Kernel Adaptive Filtering : A Comprehensive Introduction,9780470608586

Kernel Adaptive Filtering : A Comprehensive Introduction

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ISBN13: 9780470608586
ISBN10: 0470608587
Format: eBook
Pub. Date: 2010-02-01
Publisher(s): Wiley
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Table of Contents

Preface
AcknowledgmentS
Notation
Abbreviations and Symbols
Background and Preview
Supervised, Sequential, and Active Learning
Linear Adaptive Filters
Nonlinear Adaptive Filters
Reproducing Kernel Hilbert Spaces
Kernel Adaptive Filters
Summarizing Remarks
Endnotes
Kernel Least-Mean-Square Algorithm
Least-Mean-Square Algorithm
Kernel Least-Mean-Square Algorithm
Kernel and Parameter Selection
Step-Size Parameter
Novelty Criterion
Self-Regularization Property of KLMS
Leaky Kernel Least-Mean-Square Algorithm
Normalized Kernel Least-Mean-Square Algorithm
Kernel ADALINE
Resource Allocating Networks
Computer Experiments
Conclusion
Endnotes
Kernel Affine Projection Algorithms
Affine Projection Algorithms
Kernel Affine Projection Algorithms
Error Reusing
Sliding Window Gram Matrix Inversion
Taxonomy for Related Algorithms
Computer Experiments
Conclusion
Endnotes
Kernel Recursive Least-Squares Algorithm
Recursive Least-Squares Algorithm
Exponentially Weighted Recursive Least-Squares Algorithm
Kernel Recursive Least-Squares Algorithm
Approximate Linear Dependency
Exponentially Weighted Kernel Recursive Least-Squares Algorithm
Gaussian Processes for Linear Regression
Gaussian Processes for Nonlinear Regression
Bayesian Model Selection
Computer Experiments
Conclusion
Endnotes
Extended Kernel Recursive Least-Squares Algorithm
Extended Recursive Least Squares Algorithm
Exponentially Weighted Extended Recursive Least Squares Algorithm
Extended Kernel Recursive Least Squares Algorithm
EX-KRLS for Tracking Models
EX-KRLS with Finite Rank Assumption
Computer Experiments
Conclusion
Endnotes
Designing Sparse Kernel Adaptive Filters
Definition of Surprise
A Review of Gaussian Process Regression
Computing Surprise
Kernel Recursive Least Squares with Surprise Criterion
Kernel Least Mean Square with Surprise Criterion
Kernel Affine Projection Algorithms with Surprise Criterion
Computer Experiments
Conclusion
Endnotes
Epilogue
Appendix
Mathematical Background
Singular Value Decomposition
Positive-Definite Matrix
Eigenvalue Decomposition
Schur Complement
Block Matrix Inverse
Matrix Inversion Lemma
Joint, Marginal, and Conditional Probability
Normal Distribution
Gradient Descent
Newton's Method
Approximate Linear Dependency and System Stability
ReferenceS
Index
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