Dec 2006
1 Hour
This course is part of our eLearning Archive, which includes older courses that may not be current or as user-friendly as courses designed more recently. This course examines Information Theory and our efforts to develop an information-theoretic criterion which can be utilized in adaptive filtering and neurocomputing. The main aim of our research is to develop new signal processing techniques by going beyond the basic assumptions of Linearity, Gaussianity, and Stationarity. By capturing higher order statistics of data using Information Theory, we solve a variety of problems in Biomedical Signal Processing, Communications, and Machine Learning.
Author Keywords: Euclidean and Cauchy Schwartz pdf Distances, Information forces, Information potential, Information theoretic learning, Kernel Annealing, Mermaid algorithm, Nonparameteric entropy estimation, Stochastic Information Gradient
IEEE Keywords: Annealing, Biomedical signal processing, Euclidean distance, Filtering, High order statistics, Information theory, Learning systems, Linearity, Machine learning, Signal processing
Persistent Link: https://ieeexplore.ieee.org/servlet/opac?mdnumber=EW1050 More »
Level: Intermediate
Instructor
Jose C. Principe
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 ... Show More
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