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Computer Science - Database Management & Information Retrieval | The Structure of Style - Algorithmic Approaches to Understanding Manner and Meaning (Authors and Editors)

The Structure of Style

Algorithmic Approaches to Understanding Manner and Meaning

Argamon, Shlomo, Burns, Kevin, Dubnov, Shlomo (Eds.)

2010, XVIII, 338 p.

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Shlomo Argamon is Associate Professor of Computer Science at the Illinois Institute of Technology, Chicago, IL, USA, since 2002. Prior to that, he had held academic positions at Bar-Ilan University, where he held a Fulbright Postdoctoral Fellowship (1994-96), and at the Jerusalem College of Technology. Dr. Argamon received his B.S. (1988) in Applied Mathematics from Carnegie-Mellon University, and his M.Phil. (1991) and Ph.D. (1994) in Computer Science from Yale University, where he was a Hertz Foundation Fellow. His current research interests lie mainly in the use of machine learning methods to aid in functional analysis of natural language, with particular focus on questions of style. During his career, Dr. Argamon has worked on a variety of problems in experimental machine learning, including robotic map-learning, theory revision, and natural language processing, and has published numerous research papers in these areas.

Kevin Burns is a Principal Scientist at the MITRE Corporation. His interest is in computational modeling of cognitive processing, including strategic decisions and visual perception, to improve the design of decision support systems. Kevin holds engineering degrees from the Massachusetts Institute of Technology where he also studied cognitive science and media arts.

Shlomo Dubnov is an Associate Professor in music technology at UCSD. Prior to this he served as invited researcher at the Institute for Research and Coordination of Acoustics and Music (IRCAM) in Paris and was a senior lecturer in the department of communication systems engineering at Ben-Gurion-University in Israel. He holds a PhD in Computer Science from Hebrew University in Jerusalem. His research topics include music improvisation systems, machine learning of musical style, computational aesthetics and questions of human perception and experience of fun.

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