| Abstract | The seventeenth of a series of workshops sponsored by the IEEE Signal Processing Society and organized by the Machine Learning for Signal Processing Technical Committee (MLSP-TC).
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 volume contains 73 papers presented at the Workshop, including 70 accepted submissions, an overview paper on feature selection
for genomics by Sun-Yuan Kung, our plenary speaker, and two papers from the winners of the Data Analysis Competition.
The program included papers in the following areas: genomic signal processing, pattern recognition and classification, image and video processing, blind signal processing, models, learning algorithms, and applications of machine learning. The program featured a Special Session on Genomic Signal Processing, chaired by Prof. Man-Wai Mak from Hong Kong Polytechnic University, Hong Kong. The session included four refereed papers by leading experts in the field. We also continued the tradition of the Data Analysis Competition thanks to the efforts of Deniz Erdogmus, Vince Calhoun, and Kenneth Hild. The program also included a tutorial talk on regularization path, sparsity and Pareto frontier in statistical learning, delivered by Stephane Canu from LITIS - INSA de Rouen, France. Our warmest thanks go to our tutorial speaker, and to our plenary speakers, Prof. Sun-Yuan Kung from Princeton University, USA, and Prof. Erkki Oja from Helsinki University of
Technology, Finland. Also our special thanks go to our emerging technologies keynote speaker Prof. Danilo Mandic from Imperial College, London, UK. |