------------------------------------------------------------------------- Final call for papers ------------------------------------------------------------------------- 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. -------------------------MLSP'2005 webpage: --------------------------------------------------------------------------