专家预测聚合算法的经验比较

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摘要翻译：
预测未来事件的结果是一个具有挑战性的问题，人们已经探索和尝试了各种解决方法。我们对各种在线和离线自适应算法进行了实证比较，这些算法利用从一个叫做概率体育的互联网竞赛中获得的专家概率启发来聚合专家对美国全国足球联赛五年（1319场）结果的预测。我们发现，在预测精度方面，很难比简单的平均预测提高，但在二次损失方面有改进的空间。有些令人惊讶的是，在我们收集的算法中，估计每个专家预测方差的贝叶斯估计算法表现出比简单平均最一致的优越性能。
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英文标题：
《An Empirical Comparison of Algorithms for Aggregating Expert Predictions》
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作者：
Varsha Dani, Omid Madani, David M Pennock, Sumit Sanghai, Brian
  Galebach
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  Predicting the outcomes of future events is a challenging problem for which a variety of solution methods have been explored and attempted. We present an empirical comparison of a variety of online and offline adaptive algorithms for aggregating experts' predictions of the outcomes of five years of US National Football League games (1319 games) using expert probability elicitations obtained from an Internet contest called ProbabilitySports. We find that it is difficult to improve over simple averaging of the predictions in terms of prediction accuracy, but that there is room for improvement in quadratic loss. Somewhat surprisingly, a Bayesian estimation algorithm which estimates the variance of each expert's prediction exhibits the most consistent superior performance over simple averaging among our collection of algorithms.
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PDF链接：
https://arxiv.org/pdf/1206.6814

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关键词：专家预测 Applications Intelligence Presentation Predictions prediction 人们 over outcomes simple