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007			cr nn 008mamaa
008			220601s2019 sz | s |||| 0|eng d
020			_a9783031015823
024	7		_a10.1007/978-3-031-01582-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA278.75 _b2019 EB
100	1		_aXia, Lirong _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686635
245	1	0	_aLearning and Decision-Making from Rank Data _cby Lirong Xia
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XV, 143 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aPreface -- Acknowledgments -- Introduction -- Statistical Models for Rank Data -- Parameter Estimation Algorithms -- The Rank-Breaking Framework -- Mixture Models for Rank Data -- Bayesian Preference Elicitation -- Socially Desirable Group Decision-Making from Rank Data -- Future Directions -- Bibliography -- Author's Biography.
520			_aThe ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state-of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9166090 _aAprendizaje automático _xMétodos matemáticos
650		7	_2embne _9669416 _aToma de decisiones _xProceso de datos
650		7	_2embne _9141176 _aToma de decisiones _xSimulación por ordenador
776	0	8	_iPrinted edition: _z9783031000270
776	0	8	_iPrinted edition: _z9783031004544
776	0	8	_iPrinted edition: _z9783031027109
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01582-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI