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Rule based systems for big data: a machine learning approach

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evalu...

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Detalles Bibliográficos
Autores principales: Liu, Han, Gegov, Alexander, Cocea, Mihaela
Lenguaje:eng
Publicado: Springer 2016
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-23696-4
http://cds.cern.ch/record/2112839
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author Liu, Han
Gegov, Alexander
Cocea, Mihaela
author_facet Liu, Han
Gegov, Alexander
Cocea, Mihaela
author_sort Liu, Han
collection CERN
description The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2016
publisher Springer
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spelling cern-21128392021-04-21T20:00:56Zdoi:10.1007/978-3-319-23696-4http://cds.cern.ch/record/2112839engLiu, HanGegov, AlexanderCocea, MihaelaRule based systems for big data: a machine learning approachEngineeringThe ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.Springeroai:cds.cern.ch:21128392016
spellingShingle Engineering
Liu, Han
Gegov, Alexander
Cocea, Mihaela
Rule based systems for big data: a machine learning approach
title Rule based systems for big data: a machine learning approach
title_full Rule based systems for big data: a machine learning approach
title_fullStr Rule based systems for big data: a machine learning approach
title_full_unstemmed Rule based systems for big data: a machine learning approach
title_short Rule based systems for big data: a machine learning approach
title_sort rule based systems for big data: a machine learning approach
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-23696-4
http://cds.cern.ch/record/2112839
work_keys_str_mv AT liuhan rulebasedsystemsforbigdataamachinelearningapproach
AT gegovalexander rulebasedsystemsforbigdataamachinelearningapproach
AT coceamihaela rulebasedsystemsforbigdataamachinelearningapproach