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008			171104s2018 gw | s |||| 0|eng d
020			_a9783319700588
024	7		_a10.1007/978-3-319-70058-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _bL584 2018 EB
100	1		_aLiu, Han _0http://id.loc.gov/authorities/names/n85013287 _1http://viaf.org/viaf/27032051/ _1http://dbpedia.org/resource/Emperor_Xian_of_Han _997557
245	1	0	_aGranular Computing Based Machine Learning _bA Big Data Processing Approach _cby Han Liu, Mihaela Cocea.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XV, 113 páginas 27 ilustraciones, 19 ilustraciones a color)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aStudies in Big Data _x2197-6503 _v35
490	0		_aEngineering (Springer-11647)
520	3		_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.
988			_aEBSPRINGER_2018
650		7	_aAprendizaje automático _2embne _9166090
650		7	_aDatos masivos _9495511 _2embne
700	1		_aCocea, Mihaela _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/nb2015013820 _1http://viaf.org/viaf/43146285389915370429/ _997559
776	0	8	_iEdición impresa: _z9783319700571
776	0	8	_iEdición impresa: _z9783319700595
776	0	8	_iEdición impresa: _z9783319888842
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-70058-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _ek _feng _ggw _h0