图 书 馆
计算机视觉的鲁棒分类器设计On the design of robust classifiers for computer vision |
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| 课程网址: | http://videolectures.net/cvpr2010_masnadi_shirazi_drc/ |
| 主讲教师: | Hamed Masnadi-Shirazi |
| 开课单位: | 圣地亚哥加州大学 |
| 开课时间: | 2010-07-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The design of robust classifiers, which can contend with the noisy and outlier ridden datasets typical of computer vision, is studied. It is argued that such robustness requires loss functions that penalize both large positive and negative margins. The probability elicitation view of classifier design is adopted, and a set of necessary conditions for the design of such losses is identified. These conditions are used to derive a novel robust Bayes-consistent loss, denoted Tangent loss, and an associated boosting algorithm, denoted TangentBoost. Experiments with data from the computer vision problems of scene classification, object tracking, and multiple instance learning show that TangentBoost consistently outperforms previous boosting algorithms. |
| 关 键 词: | 计算机科学; 计算机视觉; 稀疏和凸优化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-03：张荧(课程编辑志愿者) |
| 阅读次数: | 50 |