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999			_c119351 _d119351
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003			ES-MaUEC
005			20230102113943.0
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007			cr nn nnnaamaa
008			200228s2020 gw | s |||| 0|eng d
020			_a9783030310417
024	7		_a10.1007/978-3-030-31041-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA248.5 _b2020 EB
245	0	0	_aBeyond Traditional Probabilistic Data Processing Techniques : _bInterval, Fuzzy etc. Methods and Their Applications _cedited by Olga Kosheleva, Sergey P. Shary, Gang Xiang, Roman Zapatrin.
250			_aFirst edition 2020.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2020.
300			_a1 recurso en línea (XI, 649 páginas) _b142 ilustraciones, 71 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aStudies in Computational Intelligence _x1860-949X _v835
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aSymmetries are Important -- Constructive Continuity of Increasing Functions -- A Constructive Framework for Teaching Discrete Mathematics -- Fuzzy Logic for Incidence Geometry -- Strengths of Fuzzy Techniques in Data Science -- Impact of Time Delays on Networked Control of Autonomous Systems -- Sets and Systems -- An Overview of Polynomially Computable Characteristics of Special Interval Matrices -- Interval Regularization for Inaccurate Linear Algebraic Equations -- Measurable Process Selection Theorem and Non-Autonomous Inclusions -- Handling Uncertainty When Getting Contradictory Advice from Experts -- Why Sparse? -- The Kreinovich Temporal Universe -- Integral Transforms induced by Heaviside Perceptrons.
520	3		_aData processing has become essential to modern civilization. The original data for this processing comes from measurements or from experts, and both sources are subject to uncertainty. Traditionally, probabilistic methods have been used to process uncertainty. However, in many practical situations, we do not know the corresponding probabilities: in measurements, we often only know the upper bound on the measurement errors; this is known as interval uncertainty. In turn, expert estimates often include imprecise (fuzzy) words from natural language such as "small"; this is known as fuzzy uncertainty. In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination describe state-of-the-art developments in their research areas. Accordingly, the book offers a valuable guide for researchers and practitioners interested in data processing under uncertainty, and an introduction to the latest trends and techniques in this area, suitable for graduate students. .
988			_aSpringer_Robotics_31032020
650		7	_2embne _aLógica difusa _9152594
700	1		_aKosheleva, Olga _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aShary, Sergey P _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aXiang, Gang _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aZapatrin, Roman _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
776	0	8	_iPrinted edition: _z9783030310400
776	0	8	_iPrinted edition: _z9783030310424
776	0	8	_iPrinted edition: _z9783030310431
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-31041-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2020 _dz _eh _zSI