By Weifeng Liu, Jose C. Principe, Simon Haykin
ISBN-10: 0470447532
ISBN-13: 9780470447536
Kurzbeschreibung
Online studying from a sign processing perspective
There is elevated curiosity in kernel studying algorithms in neural networks and a growing to be want for nonlinear adaptive algorithms in complicated sign processing, communications, and controls. Kernel Adaptive Filtering is the 1st e-book to give a entire, unifying creation to on-line studying algorithms in reproducing kernel Hilbert areas. in response to study being performed within the Computational Neuro-Engineering Laboratory on the collage of Florida and within the Cognitive structures Laboratory at McMaster collage, Ontario, Canada, this distinct source elevates the adaptive filtering conception to a brand new point, proposing a brand new layout technique of nonlinear adaptive filters.
Covers the kernel least suggest squares set of rules, kernel affine projection algorithms, the kernel recursive least squares set of rules, the speculation of Gaussian strategy regression, and the prolonged kernel recursive least squares algorithm
provides a robust model-selection strategy referred to as greatest marginal likelihood
Addresses the central bottleneck of kernel adaptive filters--their turning out to be structure
positive aspects twelve computer-oriented experiments to augment the techniques, with MATLAB codes downloadable from the authors' internet site
* Concludes each one bankruptcy with a precis of the cutting-edge and power destiny instructions for unique research
Kernel Adaptive Filtering is perfect for engineers, computing device scientists, and graduate scholars attracted to nonlinear adaptive platforms for on-line functions (applications the place the information circulate arrives one pattern at a time and incremental optimum suggestions are desirable). it's also an invaluable advisor in the event you search for nonlinear adaptive filtering methodologies to unravel functional difficulties.
Buchrückseite
Online studying from a sign processing perspective
There is elevated curiosity in kernel studying algorithms in neural networks and a growing to be desire for nonlinear adaptive algorithms in complex sign processing, communications, and controls. Kernel Adaptive Filtering is the 1st booklet to provide a finished, unifying advent to on-line studying algorithms in reproducing kernel Hilbert areas. in keeping with study being carried out within the Computational Neuro-Engineering Laboratory on the collage of Florida and within the Cognitive platforms Laboratory at McMaster collage, Ontario, Canada, this precise source elevates the adaptive filtering conception to a brand new point, featuring a brand new layout method of nonlinear adaptive filters.
Covers the kernel least suggest squares set of rules, kernel affine projection algorithms, the kernel recursive least squares set of rules, the idea of Gaussian technique regression, and the prolonged kernel recursive least squares algorithm
provides a robust model-selection approach known as greatest marginal likelihood
Addresses the significant bottleneck of kernel adaptive filters--their growing to be structure
gains twelve computer-oriented experiments to augment the suggestions, with MATLAB codes downloadable from the authors' internet site
* Concludes each one bankruptcy with a precis of the state-of-the-art and capability destiny instructions for unique research
Kernel Adaptive Filtering is perfect for engineers, computing device scientists, and graduate scholars drawn to nonlinear adaptive platforms for on-line purposes (applications the place the information circulation arrives one pattern at a time and incremental optimum strategies are desirable). it's also an invaluable advisor when you search for nonlinear adaptive filtering methodologies to unravel functional difficulties.
