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001			103042
003			DE-He213
005			20240111050144.0
007			cr nn 008mamaa
008			180224s2018 gw | s |||| 0|eng d
020			_a9783319750491
024	7		_a10.1007/978-3-319-75049-1 _2doi
040			_aES-MaUEC _bspa
050			_aQA76.87 2018 EB
100	1		_aBuscema, Paolo Massimo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/161997681/
245	1	0	_aArtificial Adaptive Systems Using Auto Contractive Maps _bTheory, Applications and Extensions _cby Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (VII, 179 páginas 97 ilustraciones, 74 ilustraciones a color)
347			_atext file _bPDF
490	0		_aStudies in Systems, Decision and Control _x2198-4182 _v131
505	0		_aAn Introduction -- Artificial Neural Networks -- Auto-Contractive Maps -- Visualization of Auto-CM Output -- Dataset Transformations and Auto-CM -- Comparison of Auto-CM to Various Other Data Understanding Approaches.
520	3		_aThis book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and algorithms used to analyze them. It subsequently describes artificial neural networks as a subclass of artificial adaptive systems, and reports on the backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. The book's primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the "spin-net," as a dynamic form of auto-associative memory.
650		7	_aData mining _2embne _9162648
650		7	_aInteligencia artificial _2embne _9413115
650		7	_aRedes neuronales artificiales _2embne _9678664
700	1		_aMassini, Giulia _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/90351950/
700	1		_aBreda, Marco _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/313537440/
700	1		_aLodwick, Weldon A _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no2003023744 _1http://viaf.org/viaf/7074397/
700	1		_aNewman, Francis _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n84136450 _1http://viaf.org/viaf/17227308/
700	1		_aAsadi-Zeydabadi, Masoud _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no2005110650 _1http://viaf.org/viaf/78521934/
776	0	8	_iEdición impresa: _z9783319750484
776	0	8	_iEdición impresa: _z9783319750507
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-75049-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b03/2019 _dz _ek _feng _ggw _h0
999			_c103042 _d103042 _x1