Proceedings of IEEE Machine Learning for Signal Processing Workshop XVI
These proceedings contains refereed papers presented at the sixteenth IEEE Workshop on Machine Learning for Signal Processing (MLSP’2006), held in Maynooth, Co. Kildare, Ireland, September 6-8, 2006. This is a continuation of the IEEE Workshops on Neural Networks for Signal Processing (NNSP). 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 conference is organized by the Machine Learning for Signal Processing Technical Committee with sponsorship of the IEEE Signal Processing Society. Following the practice started three years ago, the bound volume of the proceedings is going to be published by IEEE following the Workshop, and we are pleased to offer to conference attendees the proceeding in a CDROM electronic format, which maintains the same standard as the printed version and facilitates the reading and searching of the papers. The field of machine learning has matured considerably in both methodology and real-world application domains and has become particularly important for solution of problems in signal processing. As reflected in this collection, machine learning for signal processing combines many ideas from adaptive signal/image processing, learning theory and models, and statistics in order to solve complex real-world signal processing applications. High quality across such topical diversity can only be maintained through a rigorous and selective review process. This year, 142 full papers (6 pages) were submitted, out of which 75 (resulting in an acceptance rate of 53%) were selected for oral or poster presentation, after reviews by three referees for each. We would like to thank the MLSP’2006 Technical Committee for taking the time to provide quality reviews. The workshop featured research work in the areas of nonlinear signal processing, system identification, blind source separation, learning theory and models, neural networks, applications in image and video processing and speech processing, as well as implementation and other applications of machine learning. The program also included a tutorial on Basics of Bayesian learning by Prof. Jan Larsen. This was also the second year for the MLSP data competition which was chaired by Deniz Erdogmus. Our warmest, special thanks go to our plenary speakers: Prasanna Mulgaonkar and Peter Raulefs from the AI Lab, Intel Cooperation, Santa Clara, Prof. Yann LeCun from the Courant Institute of Mathematical Sciences, New York University and Prof. Barak Pearlmutter from the Hamilton Institute, NUI Maynooth.