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释放你所学到的：指数衰退记忆学习者的适应性群体教学Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners |
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| 课程网址: | http://videolectures.net/kdd2018_zhou_adaptive_crowd/ |
| 主讲教师: | Yao Zhou |
| 开课单位: | 亚利桑那州立大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications. However, most existing works in crowdsourcing mainly focus on label inference and incentive design. In this paper, we address a different problem of adaptive crowd teaching, which is a sub-area of machine teaching in the context of crowdsourcing. Compared with machines, human beings are extremely good at learning a specific target concept (e.g., classifying the images into given categories) and they can also easily transfer the learned concepts into similar learning tasks. Therefore, a more effective way of utilizing crowdsourcing is by supervising the crowd to label in the form of teaching. In order to perform the teaching and expertise estimation simultaneously, we propose an adaptive teaching framework named JEDI to construct the personalized optimal teaching set for the crowdsourcing workers. In JEDI teaching, the teacher assumes that each learner has an exponentially decayed memory. Furthermore, it ensures comprehensiveness in the learning process by carefully balancing teaching diversity and learner’s accurate learning in terms of teaching usefulness. Finally, we validate the effectiveness and efficacy of JEDI teaching in comparison with the state-of-the-art techniques on multiple data sets with both synthetic learners and real crowdsourcing workers. |
| 关 键 词: | 大量标记数据的需求; 大型数据挖掘应用; 众包领域; 个性化最优教学集 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-02-03：cyh |
| 最后编审: | 2023-02-03：cyh |
| 阅读次数: | 27 |