Granular Computing Based Machine by Liu Han (24 results)
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Language: English
Published by Springer, 2017
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Published by Springer, 2017
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Published by Springer, 2017
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hardcover. Condition: Sehr gut. 128 Seiten; 9783319700571.2 Gewicht in Gramm: 500.
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Published by Springer, 2017
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Published by Springer, 2017
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Published by Springer, Berlin, Springer International Publishing, Springer, 2017
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs-Volume, Velocity, Variety, Veracity and V…ariability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries.This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.
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Language: English
Published by Springer International Publishing, 2018
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Published by Springer International Publishing, 2017
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Language: English
Published by Springer, 2017
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Language: English
Published by Springer, 2017
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Language: English
Published by Springer, 2018
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Published by Springer, 2018
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Taschenbuch. Condition: Neu. Granular Computing Based Machine Learning | A Big Data Processing Approach | Han Liu (u. a.) | Taschenbuch | Studies in Big Data | xv | Englisch | 2018 | Springer | EAN 9783319888842 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[a…t]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer, 2017
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Language: English
Published by Springer, 2017
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Language: English
Published by Springer, 2018
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Paperback. Condition: Brand New. reprint edition. 132 pages. 9.25x6.10x0.30 inches. In Stock.
Language: English
Published by Springer, 2017
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Condition: gut. 2017. Granular Computing Based Machine Learning: A Big Data Processing Approach (Studies in Big Data, 35, Band 35) In deutscher Sprache. pages.
Language: English
Published by Springer, 2018
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Published by Springer, 2017
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Published by Springer, 2018
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Condition: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days. Excellent Customer Service.
Language: English
Published by Springer, 2017
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Published by Berlin Springer International Publishing Springer Nov 2017, 2017
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs-Volume, Velocity, Variety…, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries.This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems. 113 pp. Englisch.
Language: English
Published by Springer, 2018
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Condition: New. Print on Demand pp. 132.
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Published by Springer, Birkhäuser Sep 2018, 2018
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Explores how granular computing plays a significant role in advancing machine learning towards in-depth processing of big dataIntroduces the main characteristics of big data, i.e. the five Vs-Volume, Velocity, Variety, Veracity, and Var…iabilityPresents popular types of traditional machine learning in terms of their key features and limitations in the context of big dataDiscusses the need for and different uses of granular computing based machine learningPresents several case studies of big data by using biomedical data and sentiment data, demonstrating recent advancesStresses the theoretical significance, practical importance, methodological impact, and philosophical aspectsSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch.
Language: English
Published by Springer, 2018
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Condition: New. PRINT ON DEMAND pp. 132.
