WHAT CAN ENSEMBLE OF CLASSIFIERS DO FOR YOU?
From the Signal Processing and Pattern Recognition Laboratory at Rowan University, comes a crash course on ensemble based adaptive intelligent systems like no other. What can ensemble systems do for you? How about the ability of analyzing large volumes of data with modest computing resources, partitioning large problems into smaller solvable tasks, analyzing data with missing features, predicting the confidence of an automated decision maker, avoiding model overfitting when you have very little data, incremental learning of new knowledge from streaming data without retaining or memorizing old data, learning new knowledge and knowing what to forget, when to forget and when to remember what you had forgotten, dealing with concept drift and learning from data when the data characteristics change over time – learning the rules when the rules are constantly changing, fusion of heterogeneous data sources for intelligent decision making, learning from grossly unbalanced data, proving that the collective decision of many only mildly intelligent people is statistically better than that of one very smart person, applying these concepts to early diagnosis of neurological disorders, reading someone’s thoughts, controlling devices using your thoughts via brain-machine interface. Not enough? Ok, how about giving you the ability to win $1,000,000? And, for that, … well, you will just have to come to this talk to find out how.
Robi Polikar received his B.Sc. degree in electronics and telecommunications engineering from Istanbul Technical University, Istanbul, Turkey in 1993, and his M.S. and Ph.D. degrees, both co-majors in biomedical engineering and electrical engineering, from Iowa State, in 1995 and in 2000, respectively. In 2001 he joined Electrical and Computer Engineering at the then newly established College of Engineering of Rowan University, in Glassboro, NJ, where he established the Signal Processing and Pattern Recognition Laboratory (SPPRL). In 2003, he received the National Science Foundation’s CAREER award – for developing incremental learning algorithms from streaming data. In 2009 he received a new grant from NSF to continue his work on developing algorithms that can also learn in nonstationary environments, even for severely imbalanced datasets. Most recently, he has expanded this work – supported by a new (2013) NSF grant – to semi-supervised and unsupervised learning in initially labeled environments. His current area of research interest includes adaptive intelligent systems and their various novel applications, such as incremental learning, nonstationary learning, data fusion, imbalanced data and the missing feature problem in automated decision making. He is also working on applying novel machine learning algorithms to biomedical applications, such as early diagnosis of Alzheimer’s disease, brain-computer interface, and bioinformatics. He teaches upper level undergraduate and graduate courses in wavelet theory, pattern recognition, neural networks, signal processing, and biomedical systems at Rowan. In 2012 he was awarded the Professional Progress in Engineering Award by Iowa State University, recognizing an outstanding alumnus in midcareer. He is a senior member of IEEE, and an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, for which he recently guest edited a special issue on learning in nonstationary and evolving environments. He is also a program evaluator for Accreditation Board for Engineering Technology (ABET).
Preprints / reprints of his papers, and more information on his research and teaching interests can be found at http://users.rowan.edu/~polikar.