Learning and decision-making from ra...
Xia, Lirong,

 

  • Learning and decision-making from rank data /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    杜威分類號: 519.5
    書名/作者: Learning and decision-making from rank data // Lirong Xia.
    作者: Xia, Lirong,
    出版者: [San Rafael, California] : : Morgan & Claypool,, 2019.
    面頁冊數: 1 PDF (xv, 143 pages) : : illustrations.
    附註: Part of: Synthesis digital library of engineering and computer science.
    標題: Ranking and selection (Statistics) - Data processing.
    標題: Decision making - Computer simulation.
    標題: Machine learning - Mathematical models.
    ISBN: 9781681734415
    書目註: Includes bibliographical references (pages 131-141).
    內容註: 1. Introduction -- 1.1 The research problem -- 1.2 Overview of the book --
    摘要、提要註: 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 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.
    電子資源: https://ieeexplore.ieee.org/servlet/opac?bknumber=8638925
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