图 书 馆
活跃的抽样等级学习优化估计损失减少Optimizing Estimated Loss Reduction for Active Sampling in Rank Learning |
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| 课程网址: | http://videolectures.net/icml08_donmez_oelr/ |
| 主讲教师: | Pinar Donmez |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2008-08-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Learning to rank is becoming an increasingly popular research area in machine learning. The ranking problem aims to induce an ordering or preference relations among a set of instances in the input space. However, collecting labeled data is growing into a burden in many rank applications since labeling requires eliciting the relative ordering over the set of alternatives. In this paper, we propose a novel active learning framework for SVM-based and boosting-based rank learning. Our approach suggests sampling based on maximizing the estimated loss differential over unlabeled data. Experimental results on two benchmark corpora show that the proposed model substantially reduces the labeling effort, and achieves superior performance rapidly with as much as 30% relative improvement over the margin-based sampling baseline. |
| 关 键 词: | 计算机科学; 机器学习; 学习排名 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-28：cwx |
| 阅读次数: | 23 |