000			03724nam a22004335i 4500
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
710	2		_aSpringerLink (Online service) _9106996
999			_c110498 _d110498 _x1
001			110498
003			ES-MaUEC
005			20240111050150.0
008			180728s2019 gw a o |||| 0|eng d
020			_a9783319898032
024	7		_a10.1007/978-3-319-89803-2 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQ325.5 _b2019 EB
245	0	0	_aLearning from Data Streams in Evolving Environments : _bMethods and Applications _cMoamar Sayed-Mouchaweh, editor
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019
300			_a1 recurso en línea (VIII, 317 páginas) _b131 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
490	0		_aStudies in Big Data _x2197-6503 _v41
505	0		_aChapter 1: Transfer Learning in Non-Stationary Environments -- Chapter 2: A new combination of diversity techniques in ensemble classifiers for handling complex concept drift -- Chapter 3: Analyzing and Clustering Pareto-Optimal Objects in Data Streams -- Chapter 4: Error-bounded Approximation of Data Stream: Methods and Theories -- Chapter 5: Ensemble Dynamics in Non-stationary Data Stream Classification -- Chapter 6: Processing Evolving Social Networks for Change Detection based on Centrality Measures -- Chapter 7: Large-scale Learning from Data Streams with Apache SAMOA -- Chapter 8: Process Mining for Analyzing Customer Relationship Management Systems A Case Study -- Chapter 9: Detecting Smooth Cluster Changes in Evolving Graph Sequences -- Chapter 10: Efficient Estimation of Dynamic Density Functions with Applications in Data Streams -- Chapter 11: A Survey of Methods of Incremental Support Vector Machine Learning -- Chapter 12: On Social Network-based Algorithms for Data Stream Clustering.
520	3		_aThis edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field. Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions.
988			_aPrimersemestre_2019_Engineering
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _9495511 _aDatos masivos
700	1		_aSayed-Mouchaweh, Moamar. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _997837
776	0	8	_iPrinted edition: _z9783030078621
776	0	8	_iPrinted edition: _z9783319898025
776	0	8	_iPrinted edition: _z9783319898049
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-89803-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b07/2019 _eel _zSI