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001			95987
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006			m o d
007			cr cnu|||unuuu
008			170515s2017 sz a ob 001 0 eng d
020			_a3319571133
020			_a331957115X _q(electronic bk.)
020			_a9783319571133
020			_a9783319571157 _q(electronic bk.)
020			_z9783319571133 _q(print)
040			_aGW5XE _cGW5XE _dOCLCF _dYDX _dUAB _dESU _dAZU _dUPM _dIOG _dCOO _dVT2 _dOCLCQ _dMERER _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA76.87 _bP357 2017 EB
100	1		_aPal, Sankar K., _eautor
245	1	0	_aGranular neural networks, pattern recognition and bioinformatics _cSankar K. Pal, Shubhra S. Ray, Avatharam Ganivada.
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea (xix, 227 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
490	0		_aStudies in computational intelligence _x1860-949X _vvolume 712
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas e índice
505	0		_aIntroduction to Granular Computing, Pattern Recognition and Data Mining -- Classification using Fuzzy Rough Granular Neural Networks -- Clustering using Fuzzy Rough Granular Self-Organizing Map -- Fuzzy Rough Granular Neural Network and Unsupervised Feature Selection.
520	3		_aThis book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules, . The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing.
650		7	_aRedes neuronales artificiales _2embne _0(OCoLC)fst01036260 _0 _9678664
700	1		_aGanivada, Avatharam, _eautor
700	1		_aRay, Shubhra S., _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-57115-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017D
998			_b02/2018 _dz _e- _zSI
999			_c95987 _d95987 _x1