Kernel adaptive filtering: a comprehensive introduction

This book is the first text explaining on-line learning algorithms in kernel Hilbert spaces which will have a large impact due to the recent interest in kernel learning algorithms in neural networks and the need for nonlinear adaptive algorithms in advanced signal processing, communications and in controls. The material is based on research being conducted in the Computational Neural Engineering Laboratory at the University of Florida and in the Cognitive SystemsLaboratory at McMaster University, Ontario, Canada. It is written for professionals and graduate students interested in nonlinear adaptive systems for on-line applications, i.e., applications where the data stream arrives one sample at a time and incremental optimal solutions are desirable. Another area where this class of algorithms is becoming important is in large scale machine learning problems where data sets are so large that they can not fit the computer memory, or the complexity of the calculations is beyond the reach of the computer. In such cases data must also be processed by sampling, and incrementally optimal solutions sought. All chapters will include two or three simulations toillustrate the ideas and demonstrate their applicability. MATLAB codes for this simulations will be available from the authors's web sites. The codes will be arranged in the same order as the simulations and will be straight forward for readers to redo all the results presented in the book.Jose 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 (ANNs) modeling. He is BellSouth Professor and Founder and Director of the University of Florida Computational Neuro-Engineering Laboratory (CNEL). He is involved in biomedical signal processing, in particular the electroencephalogram (EEG) and the modeling and applications of adaptive systems. Simon Haykin, PhD, is Professor and Director of Neurocomputation for Signal Processing at McMaster University. Dr. Simon Haykin is a noted authority on adaptive and learning systems. He has pioneered signal-processing techniques and systems for radar and communication applications, and authored several fundamental textbooks in those fields. Continually developing new curricula, Dr. Haykin has created innovative courses in emerging fields: neural networks, Bayesian sequential state estimation and space-time communication theory. Weifeng Liu grew up in Shanghai China. He got his B.S. andM.S. degrees in Electrical Engineering from Shanghai Jiao Tong University in 2003 and 2005 respectively. In 2005, he joined the Computational NeuroEngineering Laboratory at the University of Florida as a Ph.D. candidate. His researchfocuses on signal processing, adaptive filtering and machine learning. He has4 publications in refereed journals and 9 conference papers.

• ISBN: 978-0-470-44753-6
• Editorial: John Wiley & Sons
• Encuadernacion: Cartoné
• Páginas: 209
• Fecha Publicación: 31/03/2010
• Nº Volúmenes: 1
• Idioma: Inglés