000			04364nam a22004455i 4500
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
710	2		_aSpringerLink (Online service) _9106996
999			_c110494 _d110494 _x1
001			110494
003			ES-MaUEC
005			20230102113423.0
008			181031s2019 gw a o |||| 0|eng d
020			_a9783319985664
024	7		_a10.1007/978-3-319-98566-4 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQA76.9 .N37 _b2019 EB
245	0	0	_aNatural Computing for Unsupervised Learning _cXiangtao Li, Ka-Chun Wong, editors
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019
300			_a1 recurso en línea (VI, 273 páginas) _b121 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		_aUnsupervised and Semi-Supervised Learning _x2522-848X
505	0		_aIntroduction -- Part I - Basic Natural Computing Techniques for Unsupervised Learning -- Hard Clustering using Evolutionary Algorithms -- Soft Clustering using Evolutionary Algorithms -- Fuzzy / Rough Set Systems for Unsupervised Learning -- Unsupervised Feature Selection using Evolutionary Algorithms -- Unsupervised Feature Selection using Artificial Neural Networks -- Part II - Advanced Natural Computing Techniques for Unsupervised Learning -- Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering -- Nature-Inspired Optimization Approaches for Unsupervised Feature Selection -- Co-Evolutionary Approaches for Unsupervised Learning -- Mining Evolving Patterns using Natural Computing Techniques -- Multi-objective Optimization for Unsupervised Learning -- Many-objective Optimization for Unsupervised Learning -- Part III - Applications -- Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques -- Parallel Solution-based Natural Clustering Techniques on Railway Engineering data -- Natural Computing Techniques for Community Detection on Online Social Networks -- Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning -- Conclusion.
520	3		_aThis book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms.
988			_aPrimersemestre_2019_Engineering
650		7	_2embne _9142214 _aSistemas autoorganizativos
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _aProceso en lenguaje natural (Informática) _9158738
700	1		_aLi, Xiangtao. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aWong, Ka-Chun. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _999789
776	0	8	_iPrinted edition: _z9783030075088
776	0	8	_iPrinted edition: _z9783319985657
776	0	8	_iPrinted edition: _z9783319985671
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-98566-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b07/2019 _eel _zSI