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008			170908s2018 gw | s |||| 0|eng d
020			_a9783319617534
024	7		_a10.1007/978-3-319-61753-4 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ342 _b2018 EB
245	1	0	_aConstraint Programming and Decision Making: Theory and Applications _cedited by Martine Ceberio, Vladik Kreinovich.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XII, 128 páginas 7 ilustraciones, 1 ilustraciones a color)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aStudies in Systems, Decision and Control _x2198-4182 _v100
490	0		_aEngineering (Springer-11647)
505	0		_aWhy Deep Neural Networks: A Possible Theoretical Explanation.- From Global to Local Constraints: A Constructive Version of Bloch's Principle -- Algebraic Product is the Only t-Norm for Which Optimization Under Fuzzy Constraints is Scale-Invariant.- Automatic Loop-shaping of H ͚/μ problem in QFT using Interval Consistency based Hybrid Optimization.- Similarity Approach to Defining Basic Level of Concepts Explained from the Utility Viewpoint -- Dow Theory's Peak-and-Trough Analysis Justified.
520	3		_aThis book describes new algorithms and ideas for making effective decisions under constraints, including applications in control engineering, manufacturing (how to optimally determine the production level), econometrics (how to better predict stock market behavior), and environmental science and geosciences (how to combine data of different types). It also describes general algorithms and ideas that can be used in other application areas. The book presents extended versions of selected papers from the annual International Workshops on Constraint Programming and Decision Making (CoProd'XX) from 2013 to 2016. These workshops, held in the US (El Paso, Texas) and in Europe (Würzburg, Germany, and Uppsala, Sweden), have attracted researchers and practitioners from all over the world. It is of interest to practitioners who benefit from the new techniques, to researchers who want to extend the ideas from these papers to new application areas and/or further improve the corresponding algorithms, and to graduate students who want to learn more - in short, to anyone who wants to make more effective decisions under constraints.
988			_aEBSPRINGER_2018
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aCeberio, Martine. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/nb2014005046 _1http://viaf.org/viaf/211114899/
700	1		_aKreinovich, Vladik _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/n95102697 _1http://viaf.org/viaf/37212054/ _998177
776	0	8	_iEdición impresa: _z9783319617527
776	0	8	_iEdición impresa: _z9783319617541
776	0	8	_iEdición impresa: _z9783319871530
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-61753-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2019 _dz _ep _feng _ggw _h0