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008			160128s2016 gw | s |||| 0|eng d
020			_a9783319172903
040			_aES-MaUEC
050		4	_aQ325.5 _bC475 2016
082	0	4	_a006.3
100	1		_aChristiano Silva, Thiago _0Local _996891
245	1	0	_aMachine Learning in Complex Networks _cby Thiago Christiano Silva, Liang Zhao
250			_a1st ed.
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XVIII, 331 páginas) _b87 ilustraciones, 80 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aIntroduction -- Complex Networks -- Machine Learning -- Network Construction Techniques -- Network-Based Supervised Learning -- Network-Based Unsupervised Learning -- Network-Based Semi-Supervised Learning -- Case Study of Network-Based Supervised Learning: High-Level Data Classification -- Case Study of Network-Based Unsupervised Learning: Stochastic Competitive Learning in Networks -- Case Study of Network-Based Semi-Supervised Learning: Stochastic Competitive-Cooperative Learning in Networks.
520	3		_aThis book explores the features and advantages offered by complex networks in the domain of machine learning. In the first part of the book, we present an overview on complex networks and machine learning. Then, we provide a comprehensive description on network-based machine learning. In addition, we also address the important network construction issue. In the second part of the book, we describe some techniques for supervised, unsupervised, and semi-supervised learning that rely on complex networks to perform the learning process. Particularly, we thoroughly investigate a particle competition technique for both unsupervised and semi-supervised learning that is modeled using a stochastic nonlinear dynamical system. Moreover, we supply an analytical analysis of the model, which enables one to predict the behavior of the proposed technique. In addition, we deal with data reliability issues or imperfect data in semi-supervised learning. Even though with relevant practical importance, little research is found about this topic in the literature. In order to validate these techniques, we employ broadly accepted real-world and artificial data sets. Regarding network-based supervised learning, we present a hybrid data classification technique that combines both low and high orders of learning. The low-level term can be implemented by any traditional classification technique, while the high-level term is realized by the extraction of topological features of the underlying network constructed from the input data. Thus, the former classifies test instances according to their physical features, while the latter measures the compliance of test instances with the pattern formation of the data. We show that the high-level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn may generate broad interests to scientific community, mainly to computer science and engineering areas.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, asignarmaterias , EBSPRINGER
650		7	_aCiencia _0comprobar BNE19900974044 _2embne _9139568
650		7	_aAprendizaje automático _0(OCoLC)1004795 _2embne _0 _9166090
700	1		_aZhao, Liang _0Local _996892
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-17290-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319172903
907			_a.b12938749 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0
945			_aQ325.5 C475 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11583289 _z06-04-17
999			_c84596 _d84596 _x1