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710	2		_aSpringerLink (Online service) _9106996
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005			20240111050153.0
008			190122s2019 gw a o |||| 0|eng d
020			_a9783030107673
024	7		_a10.1007/978-3-030-10767-3 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQ325 _b2019 EB
100	1		_aRezvanian, Alireza _eautor _9670967
245	1	0	_aLearning automata approach for social networks _cby Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019
300			_a1 recurso en línea (XVII, 329 páginas) _b107 ilustraciones, 72 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aStudies in Computational Intelligence _x1860-949X _v820
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction to Learning Automata Models -- Wavefront Cellular Learning Automata: A New Learning Paradigm -- Social Networks and Learning Systems: A Bibliometric Analysis -- Social Network Sampling -- Social Community Detection -- Social Link Prediction -- Social Trust Management -- Social Recommender Systems -- Social Influence Maximization.
520	3		_aThis book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks' evolution, and to develop the algorithms required for meaningful analysis. As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _aRedes sociales _9151419
700	1		_aMoradabadi, Behnaz _eautor _9670968
700	1		_aGhavipour, Mina _eautor _9670969
700	1		_aDaliri Khomami, Mohammad Mehdi _eautor _9670970
700	1		_aMeybodi, Mohammad Reza. _eautor _9670831
776	0	8	_iPrinted edition: _z9783030107666
776	0	8	_iPrinted edition: _z9783030107680
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-10767-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _eel _zSI