In matters of great importance that has financial, medical, social or other implications, we often seek a second opinion before making a decision, sometimes a third, and sometimes many more. In doing so, we somehow weigh the individual opinions, and combine them through some thought process to reach a final decision in hope of making that decision the most informed one. The process of consulting "several experts" before making a final decision – what may be second nature to us – has recently been rediscovered by computational intelligence community for automated decision making applications, and it has emerged as a popular and heavily researched area. Also known under various other creative names, such as multiple classifier systems, committee of classifiers or mixture of experts, ensemble systems have shown to produce favorable results compared to those of single expert systems for a broad range of applications, and under a variety of scenarios. While the design, implementation and application of such systems are the main thrusts of ensemble system research, we will focus on one family of algorithm in this talk: Learn++. Learn++ is a versatile approach that is capable of incremental learning of supplementary information provided by additional data (even if such data introduce new classes), as well as data fusion of complementary information provided by independent data sources. Following an overview of various ensemble-based approaches, Learn++ and its derivatives will be explained in detail. The performance of these family of algorithms will be presented on several applications, including early diagnosis of Alzheimer's disease from event related potentials of the electroencephalogram. Oh, as for becoming a millionaire… well, for that you will have to just come and listen the talk.

Robi Polikar received his B.S. degree in electronics and communications engineering from Istanbul Technical University in 1993, M.S. and Ph.D. degrees, both co-majors in biomedical engineering and electrical engineering, from Iowa State University, Ames, Iowa, in 1995 and in 2000, respectively. He is currently an Associate Professor with the Department of Electrical and Computer Engineering at Rowan University, Glassboro, NJ. His research interests include signal processing, pattern recognition, neural systems, machine learning and computational models of learning, with applications to biomedical engineering and imaging, chemical sensing, nondestructive evaluation and testing. His specific area of interest is in ensemble systems and their various novel applications, such as incremental learning, nonstationary learning, concept drift, data fusion, confidence estimation and the missing feature problem in automated decision making. He teaches upper level undergraduate and graduate courses in wavelet theory, pattern recognition, neural networks, signal processing, and biomedical systems and devices at Rowan University. He has also taught short courses on these areas at various conferences and venues, such as the IEEE Fusion and IJCNN conferences, IEEE Philadelphia Section short courses, the FAA Tech Center, among others. He is the author of the popular wavelet tutorial, a web based course that has been used as lecture notes at many institutions around the world, and that has been translated into many languages. He is a member of IEEE and ASEE. His current work is funded primarily through NSF's CAREER program and NIH's Collaborative Research in Computational Neuroscience program. The latter funds his work in analysis of ensemble based automated decision making algorithms for the early diagnosis of Alzheimer's disease. Preprints / reprints of his papers, and more information on his research and teaching interests can be found at http://users.rowan.edu/~polikar.