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Machine learning offers an alternative approach to some
classical AI problems, such as pattern recognition, and relies mainly
on statistical modeling and self organization. Our main efforts are
focused on developing fundamental understanding of computational
learning and computational neuroscience, with applications to
man-machine communication ( handwriting, speech, natural language
modeling), motor control (robotics), vision , neurophysiology,
computational biology, and even cognitive science.
We have close contacts with many other groups at the Hebrew
university. In particular, the multidiscplinary Center for Neural
Computation, the laboratory for higher brain function at the Haddasa
Medical School, the Department of Molecular Genetics and
Biotechnology, Faculty of Medicine, the institute of life sciences,
Racah institute of physics, and the school of musicology. Our current
projects center around the following large topics:
+ Learning in human-machine interaction
+ Natural language interface to the WWW
+ Statistical analysis of neurophysiological data
+ Self-organization of proteins
+ Nonlinear acoustic signal processing
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