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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...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
Springer
2013
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-642-28699-5 http://cds.cern.ch/record/1500269 |
_version_ | 1780926873973293056 |
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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. |
id | cern-1500269 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2013 |
publisher | Springer |
record_format | invenio |
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 |