Coverart for item
The Resource Granular computing based machine learning : a big data processing approach, Han Liu, Mihaela Cocea, (electronic book)

Granular computing based machine learning : a big data processing approach, Han Liu, Mihaela Cocea, (electronic book)

Label
Granular computing based machine learning : a big data processing approach
Title
Granular computing based machine learning
Title remainder
a big data processing approach
Statement of responsibility
Han Liu, Mihaela Cocea
Creator
Contributor
Author
Subject
Language
eng
Summary
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
Member of
Cataloging source
EBLCP
http://library.link/vocab/creatorName
Liu, Han
Dewey number
  • 006.3/1
  • 620
Index
no index present
LC call number
Q325.5
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Cocea, Mihaela
Series statement
Studies in Big Data
Series volume
v. 35
http://library.link/vocab/subjectName
  • Machine learning
  • Granular computing
Label
Granular computing based machine learning : a big data processing approach, Han Liu, Mihaela Cocea, (electronic book)
Instantiates
Publication
Note
""Appendix B Results on Random Data Partitioning""
Antecedent source
file reproduced from an electronic resource
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • ""Preface""; ""Acknowledgements""; ""Contents""; ""Acronyms""; ""1 Introduction""; ""1.1 Background of Big Data""; ""1.2 Concepts of Data Science""; ""1.3 Machine Learning""; ""1.4 Granular Computing""; ""1.5 Chapters Overview""; ""References""; ""2 Traditional Machine Learning""; ""2.1 Supervised Learning""; ""2.2 Heuristic Learning""; ""2.3 Single-Task Learning""; ""2.4 Discriminative Learning""; ""2.5 Random Data Partitioning""; ""2.6 General Issues""; ""2.7 Impacts from Big Data""; ""References""; ""3 Semi-supervised Learning Through Machine Based Labelling""
  • ""3.1 Overview of Semi-supervised Learning""""3.2 Granular Framework of Learning""; ""3.3 Discussion""; ""References""; ""4 Nature Inspired Semi-heuristic Learning""; ""4.1 Overview of Semi-heuristic Learning""; ""4.2 Granular Framework of Learning""; ""4.3 Discussion""; ""References""; ""5 Fuzzy Classification Through Generative Multi-task Learning""; ""5.1 Overview of Generative Multi-task Learning""; ""5.2 Concepts of Fuzzy Classification""; ""5.3 Granular Framework of Learning""; ""5.4 Discussion""; ""References""; ""6 Multi-granularity Semi-random Data Partitioning""
  • ""6.1 Overview of Semi-random Data Partitioning""""6.2 Granular Framework of Learning""; ""6.3 Discussion""; ""References""; ""7 Multi-granularity Rule Learning""; ""7.1 Overview of Rule Learning""; ""7.2 Granular Framework of Learning""; ""7.3 Discussion""; ""References""; ""8 Case Studies""; ""8.1 Biomedical Data Processing""; ""8.2 Sentiment Analysis""; ""References""; ""9 Conclusion""; ""9.1 Scientific Aspects""; ""9.2 Philosophical Aspects""; ""9.3 Further Directions""; ""References""; ""Appendix A Results on Generative Multi-task Classification""
Control code
SPR1011166526
Dimensions
unknown
Extent
1 online resource (123 pages).
File format
one file format
Form of item
online
Isbn
9783319700571
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-70058-8
Quality assurance targets
unknown
Reformatting quality
unknown
Specific material designation
remote
System control number
  • on1011166526
  • (OCoLC)1011166526
Label
Granular computing based machine learning : a big data processing approach, Han Liu, Mihaela Cocea, (electronic book)
Publication
Note
""Appendix B Results on Random Data Partitioning""
Antecedent source
file reproduced from an electronic resource
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • ""Preface""; ""Acknowledgements""; ""Contents""; ""Acronyms""; ""1 Introduction""; ""1.1 Background of Big Data""; ""1.2 Concepts of Data Science""; ""1.3 Machine Learning""; ""1.4 Granular Computing""; ""1.5 Chapters Overview""; ""References""; ""2 Traditional Machine Learning""; ""2.1 Supervised Learning""; ""2.2 Heuristic Learning""; ""2.3 Single-Task Learning""; ""2.4 Discriminative Learning""; ""2.5 Random Data Partitioning""; ""2.6 General Issues""; ""2.7 Impacts from Big Data""; ""References""; ""3 Semi-supervised Learning Through Machine Based Labelling""
  • ""3.1 Overview of Semi-supervised Learning""""3.2 Granular Framework of Learning""; ""3.3 Discussion""; ""References""; ""4 Nature Inspired Semi-heuristic Learning""; ""4.1 Overview of Semi-heuristic Learning""; ""4.2 Granular Framework of Learning""; ""4.3 Discussion""; ""References""; ""5 Fuzzy Classification Through Generative Multi-task Learning""; ""5.1 Overview of Generative Multi-task Learning""; ""5.2 Concepts of Fuzzy Classification""; ""5.3 Granular Framework of Learning""; ""5.4 Discussion""; ""References""; ""6 Multi-granularity Semi-random Data Partitioning""
  • ""6.1 Overview of Semi-random Data Partitioning""""6.2 Granular Framework of Learning""; ""6.3 Discussion""; ""References""; ""7 Multi-granularity Rule Learning""; ""7.1 Overview of Rule Learning""; ""7.2 Granular Framework of Learning""; ""7.3 Discussion""; ""References""; ""8 Case Studies""; ""8.1 Biomedical Data Processing""; ""8.2 Sentiment Analysis""; ""References""; ""9 Conclusion""; ""9.1 Scientific Aspects""; ""9.2 Philosophical Aspects""; ""9.3 Further Directions""; ""References""; ""Appendix A Results on Generative Multi-task Classification""
Control code
SPR1011166526
Dimensions
unknown
Extent
1 online resource (123 pages).
File format
one file format
Form of item
online
Isbn
9783319700571
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-70058-8
Quality assurance targets
unknown
Reformatting quality
unknown
Specific material designation
remote
System control number
  • on1011166526
  • (OCoLC)1011166526

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