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Emerging Paradigms in Machine Learning

This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at...

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Detalles Bibliográficos
Autores principales: Ramanna, Sheela, Jain, Lakhmi, Howlett, Robert
Lenguaje:eng
Publicado: Springer 2013
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-642-28699-5
http://cds.cern.ch/record/1500269
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author Ramanna, Sheela
Jain, Lakhmi
Howlett, Robert
author_facet Ramanna, Sheela
Jain, Lakhmi
Howlett, Robert
author_sort Ramanna, Sheela
collection CERN
description This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.   
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institution Organización Europea para la Investigación Nuclear
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spelling cern-15002692021-04-22T00:01:54Zdoi:10.1007/978-3-642-28699-5http://cds.cern.ch/record/1500269engRamanna, SheelaJain, LakhmiHowlett, RobertEmerging Paradigms in Machine LearningEngineeringThis  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.   Springeroai:cds.cern.ch:15002692013
spellingShingle Engineering
Ramanna, Sheela
Jain, Lakhmi
Howlett, Robert
Emerging Paradigms in Machine Learning
title Emerging Paradigms in Machine Learning
title_full Emerging Paradigms in Machine Learning
title_fullStr Emerging Paradigms in Machine Learning
title_full_unstemmed Emerging Paradigms in Machine Learning
title_short Emerging Paradigms in Machine Learning
title_sort emerging paradigms in machine learning
topic Engineering
url https://dx.doi.org/10.1007/978-3-642-28699-5
http://cds.cern.ch/record/1500269
work_keys_str_mv AT ramannasheela emergingparadigmsinmachinelearning
AT jainlakhmi emergingparadigmsinmachinelearning
AT howlettrobert emergingparadigmsinmachinelearning