=================================================================== First Call for Papers for the Twentieth IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010) August 29 - September 1, 2010, Kittila, Finland Website: http://mlsp2010.conwiz.dk IMPORTANT DATES: Submission of full papers: April 1, 2010 Notification of acceptance: May 28, 2010 Camera-ready paper and author registration: June 18, 2010 Advance registration before: June 23, 2010 =================================================================== The 2010 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP 2010) will be held in Kittila, Finland, in August-September 2010. MLSP 2010 is the twentieth workshop in the series of workshops sponsored by IEEE Signal Processing Society. It will present the most recent and exciting contributions in machine learning for signal processing through keynote talks as well as special and regular single-track sessions. INVITED SPEAKERS: - Prof. Zoubin Ghahramani, University of Cambridge - Prof. Tom Mitchell, Carnegie Mellon University - Dr. Henry Tirri, Head of Nokia Research Center ORGANIZATION: General chair: Erkki Oja Program chairs: Samuel Kaski, David Miller Special session chairs: Samy Bengio, Mikko Kurimo Publicity chairs: Marc Van Hulle, Jaakko Peltonen Web and publication chairs: Antti Honkela, Jan Larsen Data competition chairs: Vince Calhoun, Kenneth Hild, Mikko Kurimo Local arrangements: Tapani Raiko (chair), Francesco Corona, Ali Faisal, Mari-Sanna Paukkeri VENUE: MLSP 2010 will be held in the Levi Summit conference and exhibition centre in Kittila, Finland. Levi is one of the largest resorts in Finnish Lapland, north of the Arctic Circle. In the summer, Levi offers many sports activities as well as lots of wild northern nature. The conference centre is located high on the hillside of the Levi fell, accessible by gondola from the main village. CONFERENCE TOPICS: Machine learning in signal processing is concerned with tasks such as detection, estimation, prediction, classification, and optimization, with a wide range of applications. The following is a non-exhaustive list of topics for MLSP 2010: - Bayesian learning and signal processing - Cognitive information processing - Graphical and kernel methods - Information-theoretic learning - Learning theory and algorithms, including bounds on performance - Supervised learning, including signal detection, pattern recognition and classification - Unsupervised learning, reinforcement learning - Source separation and component analysis - Data fusion and integration - Feature extraction, information visualization - Sparse and structured representations - Neural network learning - Time-series analysis - Adaptive filtering - Data mining, information retrieval - Sequential learning and sequential decision methods - Hardware implementation of machine learning in signal processing - Applications of machine learning: Bioinformatics, Biomedical and neural signal processing, Neuroinformatics, Speech and audio processing, Image and video processing, Computer vision, Sensor networks, Robot control, Communications, Cognitive radio, Multimodal interfaces and context modeling, Intelligent multimedia and web processing SPECIAL SESSION: A special session "Towards multimodal proactive interfaces using large-scale machine learning" is being organized. For more information see http://mlsp2010.conwiz.dk . DATA COMPETITION: In conjunction with the workshop, a data and signal analysis competition is being organized. Winners will present their works and receive their award during the Workshop. PAPER SUBMISSION PROCEDURE: Authors are invited to submit a double column paper of up to six pages using the electronic submission procedure described at http://mlsp2010.conwiz.dk Accepted papers will be published by IEEE Press and electronic proceedings will be distributed at the Workshop. SPONSORS: MLSP 2010 is supported by IEEE, by the IEEE Signal Processing Society, and by the PASCAL2 Network of Excellence. ========= See http://mlsp2010.conwiz.dk for more details! =========