Kernel Adaptive Filtering
A Comprehensive Introduction
Online learning from a signal processing perspectiveThere 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 filterin…
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Produktdetails
Weitere Autoren: Liu, Weifeng / Haykin, Simon
- ISBN: 978-0-470-44753-6
- EAN: 9780470447536
- Produktnummer: 8870714
- Verlag: Wiley
- Sprache: Englisch
- Erscheinungsjahr: 2010
- Seitenangabe: 240 S.
- Masse: H23.8 cm x B16.4 cm x D1.8 cm 455 g
- Reihenbandnummer: 1
- Gewicht: 455
Über den Autor
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.
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