Reproducing kernel Hilbert spaces is a topic of great current interest for applications in signal processing, communications, and controls The first book to explain real-time learning algorithms in reproducing kernel Hilbert spaces,
On-Line Kernel Learning includes simulations that illustrate the ideas discussed and demonstrate their applicability as well as MATLAB codes for simulations. This book is ideal for professionals and graduate students interested in nonlinear adaptive systems for on-line applications.
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.
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 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.