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可靠众包系统的迭代学习

Iterative Learning for Reliable Crowdsourcing Systems
课程网址: http://videolectures.net/nips2011_oh_crowdsourcing/  
主讲教师: Sewoong Oh
开课单位: 伊利诺伊大学
开课时间: 2012-01-25
课程语种: 英语
中文简介:
众包系统,其中任务以电子方式分发给众多“信息工作者”,已经成为人类有效解决图像分类,数据输入,光学字符识别,推荐和校对等领域中的大规模问题的有效范例。由于这些低薪工人可能不可靠,因此几乎所有众包商都必须设计方案以增加对答案的信心,通常是通过多次分配每项任务并以某种方式组合答案,例如多数投票。在本文中,我们考虑了这种众包任务的一般模型,并且提出了为实现目标整体可靠性而必须支付的总价格(即,任务分配的数量)最小化的问题。我们提供了新的算法来决定分配给哪些工作人员的任务以及从工人的答案中推断出正确的答案。我们证明了我们的算法明显优于大多数投票,事实上,通过与知道每个工人可靠性的神谕相比,它是渐近最优的。
课程简介: Crowdsourcing systems, in which tasks are electronically distributed to numerous "information piece-workers", have emerged as an effective paradigm for human-powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading. Because these low-paid workers can be unreliable, nearly all crowdsourcers must devise schemes to increase confidence in their answers, typically by assigning each task multiple times and combining the answers in some way such as majority voting. In this paper, we consider a general model of such crowdsourcing tasks, and pose the problem of minimizing the total price (i.e., number of task assignments) that must be paid to achieve a target overall reliability. We give new algorithms for deciding which tasks to assign to which workers and for inferring correct answers from the workers’ answers. We show that our algorithm significantly outperforms majority voting and, in fact, are asymptotically optimal through comparison to an oracle that knows the reliability of every worker.
关 键 词: 众包系统; 电子方式; 图像分类
课程来源: 视频讲座网
最后编审: 2020-07-13:yumf
阅读次数: 53