众包预测问题的协作机制A Collaborative Mechanism for Crowdsourcing Prediction Problems |
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课程网址: | http://videolectures.net/nips2011_abernethy_prediction/ |
主讲教师: | Jacob Abernethy |
开课单位: | 加州大学伯克利分校 |
开课时间: | 2012-01-25 |
课程语种: | 英语 |
中文简介: | Netflix奖等机器学习竞赛已被证明是一种“众包”预测任务的合理成功方法。但这些比赛有许多弱点,特别是在他们为参赛者创造的激励结构方面。我们提出了一种称为群众学习机制的新方法,其中参与者协作地“学习”给定预测任务的假设。该方法大量借鉴了预测市场的概念,交易者押注于未来事件的可能性。在我们的框架中,该机制继续发布当前假设,参与者可以通过更新投注来修改此假设。关键激励属性是参与者将获得一定数量的金额,该金额根据她的更新在已发布的测试集上提高性能的程度进行扩展。 |
课程简介: | Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of “crowdsourcing” prediction tasks. But these competitions have a number of weaknesses, particularly in the incentive structure they create for the participants. We propose a new approach, called a Crowdsourced Learning Mechanism, in which participants collaboratively “learn” a hypothesis for a given prediction task. The approach draws heavily from the concept of a prediction market, where traders bet on the likelihood of a future event. In our framework, the mechanism continues to publish the current hypothesis, and participants can modify this hypothesis by wagering on an update. The critical incentive property is that a participant will profit an amount that scales according to how much her update improves performance on a released test set. |
关 键 词: | 机器学习竞赛; 激励结构; 预测市场 |
课程来源: | 视频讲座网 |
最后编审: | 2019-07-26:cwx |
阅读次数: | 28 |