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Special Issue on Machine Learning Methods in Signal Processing
August 2004
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Much of modern statistical signal processing relies on learning
algorithms of one form or another. While some of the rich
literature on machine learning has penetrated the signal processing
community in great depth, a variety of new techniques, which offer
tremendous potential for signal processing applications, have not
enjoyed similar broad acceptance in the signal processing
community. Raising awareness and surveying the state of art of
machine learning for signal processing applications and theory is
both timely and important. This Call for Papers is directed to both
the signal processing and the machine learning research
communities, based on our belief that great synergies can be
realized by further stimulating the cross-fertilization that has been
ongoing between these strong research communities.
We solicit papers from researchers in signal processing,
who apply machine learning methods and approaches to solve
difficult problems in signal processing. These include problems of
detection and estimation in the presence of significant
environmental or model uncertainty, channel estimation and
equalization, pattern recognition and classification, and other
signal processing problems of practical and theoretical interest.
We also solicit papers from researchers in machine learning, who
are working on topics with a signal processing aspect, such as
problems of prediction, estimation, data compression, pattern
recognition and analysis, sequential decision theory, machine
vision, and classification. The scope of this Special Issue of the
IEEE Transactions on Signal Processing covers applications of
machine learning methods in signal processing, where the
learning aspects of the problem are of particular interest. Papers
on machine learning that have limited signal processing focus are
out of the scope of this call. Similarly, signal processing papers
with limited emphasis on learning are also not encouraged.
We call for high-quality innovative research papers and also for review papers in the broad signal processing areas described above. |
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| Topics: |
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New methods in machine learning applied to signal processing applications
Sequential learning / decision methods and causal learning in adaptive signal processing applications
PAC learning methods
Bayesian learning methods
Graphical models and iterative algorithms
Decentralized and multi-agent learning methods
Min-max methods, asymptotics, and bounds on performance
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Editors: |
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Meir Feder, Tel Aviv University, Ramat Aviv 69978, ISRAEL
Mario A.T. Figueiredo Institute of Telecommunications, Instituto Superior Tecnico, 1049-001 Lisboa, Portugal
Alfred O. Hero, Univ. of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109-2122
Chin-Hui Lee, Van Leer Building, 777 Atlantic Drive NW, Atlanta, GA 30332-0360
Hans-Andrea Loeliger (ISI) ETF E101, ETH Zentrum, Sternwartstrasse 7, CH-8092 Zόrich, Switzerland
Robert Nowak, Rice University, 2023 Duncan Hall , Houston, TX USA
Andrew C. Singer, University of Illinois, 1308 W. Main Street, Urbana, IL 61820
Bin Yu, Department of Statistics, University of California, Berkeley, CA 94720-3860
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