图 书 馆
基于优化的动态批模式主动学习框架An Optimization Based Framework for Dynamic Batch Mode Active Learning |
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| 课程网址: | http://videolectures.net/nipsworkshops2010_chakraborty_obf/ |
| 主讲教师: | Shayok Chakraborty |
| 开课单位: | 亚利桑那州立大学 |
| 开课时间: | 2011-01-13 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Active learning techniques have gained popularity in reducing human effort to annotate data instances for inducing a classifier. When faced with large quantities of unlabeled data, such algorithms automatically select the salient and representative samples for manual annotation. Batch mode active learning schemes have been recently proposed to select a batch of data instances simultaneously, rather than updating the classifier after every single query. While numerical optimization strategies seem a natural choice to address this problem (by selecting a batch of points to ensure that a given objective criterion is optimized), many of the proposed approaches are based on greedy heuristics. Also, all the existing work on batch mode active learning assume that the batch size is given as an input to the problem. In this work, we propose a novel optimization based strategy to dynamically decide the batch size as well as the specific points to be queried, based on the particular data stream in question. Our results on the widely used VidTIMIT and the MBGC biometric datasets corroborate the efficacy of the framework to adaptively identify the batch size and the particular data points to be selected for manual annotation, in any batch mode active learning application. |
| 关 键 词: | 主动学习技术; 注释数据; 批处理模式 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-07：lxf |
| 阅读次数: | 50 |