000 03987nam a22005295i 4500
001 978-3-031-01582-3
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020 _a9783031015823
_9978-3-031-01582-3
024 7 _a10.1007/978-3-031-01582-3
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aXia, Lirong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987599
245 1 0 _aLearning and Decision-Making from Rank Data
_h[electronic resource] /
_cby Lirong Xia.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXV, 143 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_987601
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
710 2 _aSpringerLink (Online service)
_987604
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000270
776 0 8 _iPrinted edition:
_z9783031004544
776 0 8 _iPrinted edition:
_z9783031027109
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_987605
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01582-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c86123
_d86123