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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Granular Computing Based Machine Learning - A Big Data Processing Approach (Hardcover, 1st ed. 2018)
Loot Price: R4,061
Discovery Miles 40 610
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Granular Computing Based Machine Learning - A Big Data Processing Approach (Hardcover, 1st ed. 2018)
Series: Studies in Big Data, 35
Expected to ship within 10 - 15 working days
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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.
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