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 DemandForecasting Team at Amazon.com Inc. His research interests includekernel adaptive filtering, online active learning, and solvingreal-life large-scale data mining problems. Jose C. Principe is Distinguished Professor ofElectrical and Biomedical Engineering at the University of Florida,Gainesville, where he teaches advanced signal processing andartificial neural networks modeling. He is BellSouth Professor andfounder and Director of the University of Florida ComputationalNeuro-Engineering Laboratory. Simon Haykin is Distinguished University Professor atMcMaster University, Canada.He is world-renowned for hiscontributions to adaptive filtering applied to radar andcommunications. Haykin's current research passion is focused oncognitive dynamic systems, including applications on cognitiveradio and cognitive radar.