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Listen to Coronavirus Patient Zero

A Week At Waterloo In 1815

RRP $18.99

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This is a reproduction of a book published before 1923. This book may have occasional imperfections such as missing or blurred pages, poor pictures, errant marks, etc. that were either part of the original artifact, or were introduced by the scanning process. We believe this work is culturally important, and despite the imperfections, have elected to bring it back into print as part of our continuing commitment to the preservation of printed works worldwide. We appreciate your understanding of the imperfections in the preservation process, and hope you enjoy this valuable book.


Kernel Adaptive Filtering

RRP $174.95

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<b>Online learning from a signal processing perspective</b> <p> 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. <i>Kernel Adaptive Filtering</i> 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. <ul> <li> <p> 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 <li> <p> Presents a powerful model-selection method called maximum marginal likelihood <li> <p> Addresses the principal bottleneck of kernel adaptive filters&#8212;their growing structure <li> <p> Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site <li> <p> Concludes each chapter with a summary of the state of the art and potential future directions for original research </ul> <p> <i>Kernel Adaptive Filtering</i> 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.



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