使用一致性惩罚聚合不同的判断

Guanchun Wang, S. Kulkarni, H. Poor
{"title":"使用一致性惩罚聚合不同的判断","authors":"Guanchun Wang, S. Kulkarni, H. Poor","doi":"10.1109/CISS.2009.5054683","DOIUrl":null,"url":null,"abstract":"In this paper, practical algorithms for solving the probabilistic judgment aggregation problem are given. First, the scalable Coherent Approximation Principle (CAP) algorithm proposed by Predd, et al., and its computational savings gained through Successive Orthogonal Projection are explained. Implications of de Finetti's theorem in this situation are also discussed. Then a coherence penalty is defined and the Coherence Penalty Weighted Principle (CPWP) is proposed to take advantage of the data structure alongside the coherence approximation. Justification is given for the guideline that more coherent judges should be given larger weights. Simulation results with Brier Scores on both a collected database and simulated data are given for comparison. In addition to the CPWP, a recursive online variant with weight updates is presented to accommodate real-time aggregation problems.","PeriodicalId":433796,"journal":{"name":"2009 43rd Annual Conference on Information Sciences and Systems","volume":"20 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Aggregating disparate judgments using a coherence penalty\",\"authors\":\"Guanchun Wang, S. Kulkarni, H. Poor\",\"doi\":\"10.1109/CISS.2009.5054683\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, practical algorithms for solving the probabilistic judgment aggregation problem are given. First, the scalable Coherent Approximation Principle (CAP) algorithm proposed by Predd, et al., and its computational savings gained through Successive Orthogonal Projection are explained. Implications of de Finetti's theorem in this situation are also discussed. Then a coherence penalty is defined and the Coherence Penalty Weighted Principle (CPWP) is proposed to take advantage of the data structure alongside the coherence approximation. Justification is given for the guideline that more coherent judges should be given larger weights. Simulation results with Brier Scores on both a collected database and simulated data are given for comparison. In addition to the CPWP, a recursive online variant with weight updates is presented to accommodate real-time aggregation problems.\",\"PeriodicalId\":433796,\"journal\":{\"name\":\"2009 43rd Annual Conference on Information Sciences and Systems\",\"volume\":\"20 2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-03-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 43rd Annual Conference on Information Sciences and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISS.2009.5054683\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 43rd Annual Conference on Information Sciences and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS.2009.5054683","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文给出了求解概率判断聚合问题的实用算法。首先,介绍了Predd等人提出的可扩展相干逼近原理(scalable Coherent Approximation Principle, CAP)算法及其通过连续正交投影节省的计算量。本文还讨论了德菲内蒂定理在这种情况下的含义。然后定义了相干惩罚,提出了相干惩罚加权原则(coherence penalty Weighted Principle, CPWP)来利用数据结构和相干逼近的优势。为指导原则提供了理由,即应该给予更连贯的法官更大的权重。在收集的数据库和模拟数据上给出了Brier分数的模拟结果进行比较。除了CPWP之外,还提出了一种具有权重更新的递归在线变体,以适应实时聚合问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Aggregating disparate judgments using a coherence penalty
In this paper, practical algorithms for solving the probabilistic judgment aggregation problem are given. First, the scalable Coherent Approximation Principle (CAP) algorithm proposed by Predd, et al., and its computational savings gained through Successive Orthogonal Projection are explained. Implications of de Finetti's theorem in this situation are also discussed. Then a coherence penalty is defined and the Coherence Penalty Weighted Principle (CPWP) is proposed to take advantage of the data structure alongside the coherence approximation. Justification is given for the guideline that more coherent judges should be given larger weights. Simulation results with Brier Scores on both a collected database and simulated data are given for comparison. In addition to the CPWP, a recursive online variant with weight updates is presented to accommodate real-time aggregation problems.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Molecular recognition as an information channel: The role of conformational changes Extrinsic tree decoding Message transmission and state estimation over Gaussian broadcast channels Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements Speech enhancement using the multistage Wiener filter
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1