| 书目名称 | Learning and Decision-Making from Rank Data | | 编辑 | Lirong Xia | | 视频video | | | 丛书名称 | Synthesis Lectures on Artificial Intelligence and Machine Learning | | 图书封面 |  | | 描述 | .The 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 con | | 出版日期 | Book 2019 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01582-3 | | isbn_softcover | 978-3-031-00454-4 | | isbn_ebook | 978-3-031-01582-3Series ISSN 1939-4608 Series E-ISSN 1939-4616 | | issn_series | 1939-4608 | | copyright | Springer Nature Switzerland AG 2019 |
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发表于 2025-3-21 19:20:18
