Georgieva P., Mihaylova L., Jain L.C. (eds.) Advances in Intelligent Signal Processing and Data Mining. Theory and Applications

Springer, 2013. — 359 p.Dealing with large amounts of data and reasoning in real time are some of the challenges that our everyday 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.
This research book is directed to the research students, professors, researchers and practitioners interested in exploring the advanced techniques in intelligent signal processing and data mining paradigms.Introduction to Intelligent Signal Processing and Data Mining.
Monte Carlo-Based Bayesian Group Object Tracking and Causal Reasoning.
A Sequential Monte Carlo Method for Multi-target Tracking with the Intensity Filter.
Sequential Monte Carlo Methods for Localization in Wireless Networks.
A Sequential Monte Carlo Approach for Brain Source Localization.
Computational Intelligence in Automotive Applications.
Detecting Anomalies in Sensor Signals Using Database Technology.
Hierarchical Clustering for Large Data Sets.
A Novel Framework for Object Recognition under Severe Occlusion.
Historical Consistent Neural Networks: New Perspectives on Market Modeling, Forecasting and Risk Analysis.
Reinforcement Learning with Neural Networks: Tricks of the Trade.
Sliding Empirical Mode Decomposition-Brain Status Data Analysis and Modeling.