Druck auf Anfrage Neuware - Printed after ordering - This state-of-the-art resourcebrings together the latest findingsfrom the cross-fertilization of signal processing, machine learningand computer science. The emphasis is on demonstrating synergy of different signal processing methods with knowledge extraction and heterogeneous information fusion. Issues related to the processing of signals with low signal-to-noise ratio, solving real-world multi-channel problems, and using adaptive techniques where nonstationarity, uncertainty and complexity play major roles are addressed. Particular methods include Independent Component Analysis, Support Vector Machines, Distributed and Collaborative Adaptive Filtering, Empirical Mode Decomposition, Self Organizing Maps, Fuzzy Logic, Evolutionary Algorithms and several othersused frequently in thesefields. Also included arebothimportant and novel applications from telecommunications, renewable energy and biomedical engineering.Signal Processing Techniques for Knowledge Extraction and Information Fusionwhich proposes new techniques for extracting knowledge based on combining heterogeneous information sources is an excellent reference for professionals in signal and image processing, machine learning, data and sensor fusion, computational intelligence, knowledge discovery, pattern recognition, and environmental science and engineering.

N� de ref. del art�culo 9781441944955

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Detalles bibliogr�ficos

T�tulo: Signal Processing Techniques for Knowledge Extraction and Information Fusion
Editor: Springer US, Springer US
Fecha de publicaci�n: 2010
Idioma: Ingl�s
ISBN 10: 1441944958
ISBN 13: 9781441944955
Encuadernaci�n: Taschenbuch
Estado: Neu

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Sinopsis

This book brings together the latest research achievements from various areas of signal processing and related disciplines. It aims to present knowledge extraction and information fusion supported by state-of-the-art background material. Within the book contributions presenting both novel algorithms and existing applications, especially those (but not restricted to) on-line processing of real world data are included. The areas of Knowledge Extraction and Information Fusion are naturally linked, and the subject is of major interest for modern biomedical, environmental, and industrial applications. This provides the state-of-the-art and proposes new techniques in order to combine heterogeneous information sources.

De la contraportada

This state-of-the-art resource brings together the latest findings from the cross-fertilization of signal processing, machine learning and computer science. The emphasis is on demonstrating synergy of different signal processing methods with knowledge extraction and heterogeneous information fusion. Issues related to the processing of signals with low signal-to-noise ratio, solving real-world multi-channel problems, and using adaptive techniques where nonstationarity, uncertainty and complexity play major roles are addressed. Particular methods include Independent Component Analysis, Support Vector Machines, Distributed and Collaborative Adaptive Filtering, Empirical Mode Decomposition, Self Organizing Maps, Fuzzy Logic, Evolutionary Algorithms and several others used frequently in these fields. Also included are both important and novel applications from telecommunications, renewable energy and biomedical engineering.

Signal Processing Techniques for Knowledge Extraction and Information Fusion which proposes new techniques for extracting knowledge based on combining heterogeneous information sources is an excellent reference for professionals in signal and image processing, machine learning, data and sensor fusion, computational intelligence, knowledge discovery, pattern recognition, and environmental science and engineering.

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