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010   $a3-319-70058-8
024 7 $a10.1007/978-3-319-70058-8
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035   $a(DE-He213)978-3-319-70058-8
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035   $a(PPN)221253289
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100   $a20171104d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 10$aGranular Computing Based Machine Learning $eA Big Data Processing Approach /$fby Han Liu, Mihaela Cocea
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (XV, 113 p. 27 illus., 19 illus. in color.) 
225 1 $aStudies in Big Data,$x2197-6503 ;$v35
311   $a3-319-70057-X 
320   $aIncludes bibliographical references at the end of each chapters.
330   $aThis book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs?Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data.     Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries. This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.
410  0$aStudies in Big Data,$x2197-6503 ;$v35
606   $aComputational intelligence
606   $aBig data
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070
615  0$aComputational intelligence.
615  0$aBig data.
615 14$aComputational Intelligence.
615 24$aBig Data.
615 24$aBig Data/Analytics.
676   $a006.3
700   $aLiu$b Han$4aut$4http://id.loc.gov/vocabulary/relators/aut$0665835
702   $aCocea$b Mihaela$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910741175003321
996   $aGranular Computing Based Machine Learning$93554353
997   $aUNINA