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020 _a9783319700588
_9978-3-319-70058-8
024 7 _a10.1007/978-3-319-70058-8
_2doi
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072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
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082 0 4 _a006.3
_223
100 1 _aLiu, Han.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_956275
245 1 0 _aGranular Computing Based Machine Learning
_h[electronic resource] :
_bA Big Data Processing Approach /
_cby Han Liu, Mihaela Cocea.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXV, 113 p. 27 illus., 19 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Big Data,
_x2197-6511 ;
_v35
520 _aThis 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.
650 0 _aComputational intelligence.
_97716
650 0 _aBig data.
_94174
650 0 _aQuantitative research.
_94633
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aBig Data.
_94174
650 2 4 _aData Analysis and Big Data.
_956276
700 1 _aCocea, Mihaela.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_956277
710 2 _aSpringerLink (Online service)
_956278
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319700571
776 0 8 _iPrinted edition:
_z9783319700595
776 0 8 _iPrinted edition:
_z9783319888842
830 0 _aStudies in Big Data,
_x2197-6511 ;
_v35
_956279
856 4 0 _uhttps://doi.org/10.1007/978-3-319-70058-8
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79717
_d79717