Advances in Intelligent Signal Processing and Data Mining: Theory and Applications

The book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations such as sequential Monte Carl...

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Detalles Bibliográficos
Autores principales: Georgieva, Petia, Mihaylova, Lyudmila, Jain, Lakhmi
Lenguaje:eng
Publicado: Springer 2013
Materias:
Engineering
Acceso en línea:https://dx.doi.org/10.1007/978-3-642-28696-4
http://cds.cern.ch/record/1500268
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author Georgieva, Petia
Mihaylova, Lyudmila
Jain, Lakhmi
author_facet Georgieva, Petia
Mihaylova, Lyudmila
Jain, Lakhmi
author_sort Georgieva, Petia
collection CERN
description The book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations 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 and Singular Spectrum Analysis.   The 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.  
id cern-1500268
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
publisher Springer
record_format invenio
spelling cern-15002682021-04-22T00:01:55Zdoi:10.1007/978-3-642-28696-4http://cds.cern.ch/record/1500268engGeorgieva, PetiaMihaylova, LyudmilaJain, LakhmiAdvances in Intelligent Signal Processing and Data Mining: Theory and ApplicationsEngineeringThe book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations 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 and Singular Spectrum Analysis.   The 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.  Springeroai:cds.cern.ch:15002682013
spellingShingle Engineering
Georgieva, Petia
Mihaylova, Lyudmila
Jain, Lakhmi
Advances in Intelligent Signal Processing and Data Mining: Theory and Applications
title Advances in Intelligent Signal Processing and Data Mining: Theory and Applications
title_full Advances in Intelligent Signal Processing and Data Mining: Theory and Applications
title_fullStr Advances in Intelligent Signal Processing and Data Mining: Theory and Applications
title_full_unstemmed Advances in Intelligent Signal Processing and Data Mining: Theory and Applications
title_short Advances in Intelligent Signal Processing and Data Mining: Theory and Applications
title_sort advances in intelligent signal processing and data mining: theory and applications
topic Engineering
url https://dx.doi.org/10.1007/978-3-642-28696-4
http://cds.cern.ch/record/1500268
work_keys_str_mv AT georgievapetia advancesinintelligentsignalprocessinganddataminingtheoryandapplications
AT mihaylovalyudmila advancesinintelligentsignalprocessinganddataminingtheoryandapplications
AT jainlakhmi advancesinintelligentsignalprocessinganddataminingtheoryandapplications

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