图 书 馆
通过主导集进行实例选择的多实例学习Multiple-Instance Learning with Instance Selection via Dominant Sets |
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| 课程网址: | http://videolectures.net/simbad2011_erdem_dominant/ |
| 主讲教师: | İbrahim Aykut Erdem |
| 开课单位: | 哈塞佩特大学 |
| 开课时间: | 2011-10-17 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Multiple-instance learning (MIL) deals with learning under ambiguity, in which patterns to be classified are described by bags of instances. There has been a growing interest in the design and use of MIL algorithms as it provides a natural framework to solve a wide variety of pattern recognition problems. In this paper, we address MIL from a view that transforms the problem into a standard supervised learning problem via instance selection. The novelty of the proposed approach comes from its selection strategy to identify the most representative examples in the positive and negative training bags, which is based on an effective pairwise clustering algorithm referred to as dominant sets. Experimental results on both standard benchmark data sets and on multi-class image classification problems show that the proposed approach is not only highly competitive with state-of-the-art MIL algorithms but also very robust to outliers and noise. |
| 关 键 词: | 多实例学习; 算法的设计; 成对聚类算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-05：yumf |
| 阅读次数: | 52 |