Machine Learning (ML) is a generic term referring to methods and algorithms that learn based on empirical observations. In recent years, the field has matured considerably in both methodology and real-world application domains. ML methods belong not only to the classical supervised, unsupervised, or reinforcement learning paradigms (often associated with neural networks) but also to an increasingly wide range of methodologies including kernel methods, support vector machines (SVMs), Bayesian learning, etc. Also ML has become particularly important for the solution of problems in signal processing. Machine learning for signal processing combines many ideas from adaptive signal/image processing, optimization theory, learning theory and models, and statistics in order to solve complex real-world signal processing applications. The range of applications is also growing, including pattern recognition, adaptive filtering, computer vision, content based image and video retrieval, data mining, cognitive radio, robot control, data fusion, blind signal processing, sparse component analysis, brain-computer interfaces, etc. This special issue has been put together from extended versions of papers presented in the 2007 IEEE International Workshop on Machine Learning for Signal Processing (MLSP-2007), held in Thessaloniki, Greece, between August 27 and 29, 2007. The guest editor committee invited the authors of the top ranking papers—based on the review scores they received at MLSP-2007—to submit their extended papers to the special issue. The committee also decided to invite the keynote and plenary speakers to submit their contributions in a full paper format. All papers went through a regular reviewing process and were duly revised, if necessary, prior to acceptance. Since the workshop covered the overall area of machine learning the papers represent a wide range of topics including feature-extraction and classification, nonlinear learning methods, speech separation, image and video processing, inference and programming methods. We would like to take this opportunity to thank the rest of the organizing committee of the workshop for their help and support: Tulay Adali, University of Maryland, Baltimore County, USA, Jan Larsen, Technical University of Denmark, Theophilos Papadimitriou, University of Thrace, Greece, Marc Van Hulle, Katholieke Universiteit Leuven, Belgium, Scott Douglas, Southern Methodist University, TX, USA, and Deniz Erdogmus, Oregon Health & Science University, OR, USA. I. Pitas (*) Aristotle University of Thessaloniki, Thessaloniki 54124, Greece e-mail: pitas@aiia.csd.auth.gr
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Jan 1, 2011
Open Access