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Nonlinear estimation and classification

Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data This is due in part to recent advances in data collection and computing technologies As a result, fundamental statistical research is being undertaken in a variety of...

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Detalles Bibliográficos
Autores principales: Denison, David, Hansen, Mark, Holmes, Christopher, Mallick, Bani, Yu, Bin
Lenguaje:eng
Publicado: Springer 2003
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-0-387-21579-2
http://cds.cern.ch/record/1663979
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author Denison, David
Hansen, Mark
Holmes, Christopher
Mallick, Bani
Yu, Bin
author_facet Denison, David
Hansen, Mark
Holmes, Christopher
Mallick, Bani
Yu, Bin
author_sort Denison, David
collection CERN
description Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data This is due in part to recent advances in data collection and computing technologies As a result, fundamental statistical research is being undertaken in a variety of different fields Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future
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institution Organización Europea para la Investigación Nuclear
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spelling cern-16639792021-04-21T21:18:57Zdoi:10.1007/978-0-387-21579-2http://cds.cern.ch/record/1663979engDenison, DavidHansen, MarkHolmes, ChristopherMallick, BaniYu, BinNonlinear estimation and classificationMathematical Physics and MathematicsResearchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data This is due in part to recent advances in data collection and computing technologies As a result, fundamental statistical research is being undertaken in a variety of different fields Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the futureSpringeroai:cds.cern.ch:16639792003
spellingShingle Mathematical Physics and Mathematics
Denison, David
Hansen, Mark
Holmes, Christopher
Mallick, Bani
Yu, Bin
Nonlinear estimation and classification
title Nonlinear estimation and classification
title_full Nonlinear estimation and classification
title_fullStr Nonlinear estimation and classification
title_full_unstemmed Nonlinear estimation and classification
title_short Nonlinear estimation and classification
title_sort nonlinear estimation and classification
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-0-387-21579-2
http://cds.cern.ch/record/1663979
work_keys_str_mv AT denisondavid nonlinearestimationandclassification
AT hansenmark nonlinearestimationandclassification
AT holmeschristopher nonlinearestimationandclassification
AT mallickbani nonlinearestimationandclassification
AT yubin nonlinearestimationandclassification