Unsupervised process monitoring and fault diagnosis with machine learning methods

This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-...

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Detalles Bibliográficos
Autores principales: Aldrich, Chris, Auret, Lidia
Lenguaje:eng
Publicado: Springer 2013
Materias:
Computing and Computers
Acceso en línea:http://cds.cern.ch/record/1606351
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author Aldrich, Chris
Auret, Lidia
author_facet Aldrich, Chris
Auret, Lidia
author_sort Aldrich, Chris
collection CERN
description This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data
id cern-1606351
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
publisher Springer
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spelling cern-16063512021-04-21T22:24:02Zhttp://cds.cern.ch/record/1606351engAldrich, ChrisAuret, LidiaUnsupervised process monitoring and fault diagnosis with machine learning methodsComputing and ComputersThis unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data Springeroai:cds.cern.ch:16063512013
spellingShingle Computing and Computers
Aldrich, Chris
Auret, Lidia
Unsupervised process monitoring and fault diagnosis with machine learning methods
title Unsupervised process monitoring and fault diagnosis with machine learning methods
title_full Unsupervised process monitoring and fault diagnosis with machine learning methods
title_fullStr Unsupervised process monitoring and fault diagnosis with machine learning methods
title_full_unstemmed Unsupervised process monitoring and fault diagnosis with machine learning methods
title_short Unsupervised process monitoring and fault diagnosis with machine learning methods
title_sort unsupervised process monitoring and fault diagnosis with machine learning methods
topic Computing and Computers
url http://cds.cern.ch/record/1606351
work_keys_str_mv AT aldrichchris unsupervisedprocessmonitoringandfaultdiagnosiswithmachinelearningmethods
AT auretlidia unsupervisedprocessmonitoringandfaultdiagnosiswithmachinelearningmethods

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