Book 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.
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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
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Presents a powerful model-selection method called maximum
marginal likelihood
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Addresses the principal bottleneck of kernel adaptive
filters-their growing structure
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Features twelve computer-oriented experiments to reinforce the
concepts, with MATLAB codes downloadable from the authors' Web site
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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.
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.
José C. Principe 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.
