图 书 馆
多实例学习的袋差异Bag Dissimilarities for Multiple Instance Learning |
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| 课程网址: | http://videolectures.net/simbad2011_tax_dissimilarities/ |
| 主讲教师: | David M. J. Tax |
| 开课单位: | 代尔夫特工业大学 |
| 开课时间: | 2011-10-17 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | When objects cannot be represented well by single feature vectors, a collection of feature vectors can be used. This is what is done in Multiple Instance learning, where it is called a bag of instances. By using a bag of instances, an object gains more internal structure than when a single feature vector is used. This improves the expressiveness of the representation, but also adds complexity to the classification of the object. This paper shows that for the situation that not a single instance determines the class label of a bag, simple bag dissimilarity measures can significantly outperform standard multiple instance classifiers. In particular a measure that computes just the average minimum distance between instances, or a measure that uses the Earth Mover’s distance, perform very well. |
| 关 键 词: | 特征向量; 多实例学习; 实例包 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-21：cwx |
| 阅读次数: | 67 |