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                          Final call for papers
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2005 IEEE International Workshop on Machine Learning for Signal Processing
(Formerly the IEEE Workshop on Neural Networks for Signal Processing)
September 28 - 30, 2005; Mystic, Connecticut, USA

                                      Paper Submission by *April 30* 2005

-------------------------------------------http://mlsp2005.conwiz.dk/---


The fifteenth of a series of IEEE workshops on Machine Learning for Signal
Processing will be held in Mystic, Connecticut
(http://www.visitconnecticut.com/mystic.html), USA. Mystic is a pleasant
town, known best for its New England seaport and aquarium.  From the
popular Mystic Seaport, a recreated 19th-century seafaring village, to the
newly updated Mystic Aquarium, where you can mingle with beluga whales,
seals and dolphins, there is something nautical at every turn. The
Colonial period buildings of Olde Mystick Village offer a few tasty
seafood restaurants, historical atmosphere and a multitude of unique gift
shops.

This is a continuation of the IEEE workshops on Neural Networks for Signal
Processing (NNSP) organized by the NNSP technical committee of the Signal
Processing society. The name of the technical committee, hence of the
workshop, was changed to Machine Learning for Signal Processing in
September 2003 to better reflect the areas represented by the technical
committee.

The workshop will feature keynote addresses, technical presentations,
special sessions and a tutorial that will be included in the registration.
Keynote addresses will be given by Andrew Barron, Simon Haykin and Barry
Horowitz (to be confirmed). The tutorial will be on engineering
applications of fixed-point theory. The special sessions are: Machine
Learning for Genomic Signal Processing, and Biomedical Imaging and Data
Fusion. There is also a data analysis competition (to be opened on May
15th, winner to present results orally at the meeting).

Papers are solicited for, but not limited to, the following areas: 

Algorithms and Architectures: Artificial neural networks, kernel methods,
committee models, Gaussian processes, independent component analysis,
advanced (adaptive, nonlinear)  signal processing, (hidden) Markov models,
Bayesian modeling, parameter estimation, generalization, optimization,
design algorithms.

Applications: Speech processing, image processing (computer vision, OCR),
multimodal interactions, multi-channel processing, intelligent multimedia
and web processing, robotics, sonar and radar, biomedical engineering,
financial analysis, time series prediction, blind source separation, data
fusion, data mining, adaptive filtering, communications, sensors, system
identification, and other signal processing and pattern recognition
applications.

Implementations: Parallel and distributed implementation, hardware design,
and other general implementation technologies.


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