Advances in Intelligent Signal Processing and Data Mining: Theory and Applications (Studies in Computational Intelligence

Georgieva, Petia and Mihaylova, Lyudmila and Jain, Lakhmi, eds. (2013) Advances in Intelligent Signal Processing and Data Mining: Theory and Applications (Studies in Computational Intelligence. Studies in Computational Intelligence . Springer Verlag, Berlin. ISBN 978-3-642-28695-7

Abstract

Dealing with large amounts of data and reasoning in real time are some of the challenges that our every day life poses to us. The answer to these questions can be given by advanced methods in signal processing and data mining which is the scope of this book. The book presents theoretical and application achievements on some of the most efficient statistical and deterministic methods for information processing (filtering, clustering, decomposition, modelling) in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches such as sequential Monte Carlo methods, Markov Chain Monte Carlo filters, Rao Blackwellization, to the biologically inspired paradigm of Neural Networks and decomposition techniques such as Empirical Mode Decomposition, Independent Component Analysis (ICA) and Singular Spectrum Analysis. Advances and new theoretical interpretations related with these techniques are detailed and illustrated on a variety of real life problems as multiple object tracking, group object tracking, localization in wireless sensor networks, brain source localization, behavior reasoning, classification, clustering, video sequence processing, and others.