Multiple instance learning via distance metric optimization
文献类型：会议论文
| 作者 | Zhao Haifeng; Cheng Jun; Jiang Jun; Tao Dacheng |
| 出版日期 | 2013 |
| 会议名称 | 2013 20th IEEE International Conference on Image Processing, ICIP 2013 |
| 会议地点 | Melbourne, VIC, Australia |
| 英文摘要 | Multiple Instance Learning (MIL) has been widely applied in practice, such as drug activity prediction, content-based image retrieval. In MIL, a sample, comprised of a set of instances, is called a bag. Labels are assigned to bags instead of instances. The uncertainty of labels on instances makes MIL different from conventional supervised single instance learning (SIL) tasks. Therefore, it is critical to learn an effective mapping to convert an MIL task to an SIL task. In this paper, we present OptMILES by learning the optimal transformation on the bag-to-instance similarity measure, exploring the optimal distance metric between instances, by an alternating minimization training procedure. We thoroughly evaluate the proposed method on both a synthetic dataset and real world datasets by comparing with representative MIL algorithms. The experimental results suggest the effectiveness of OptMILES. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4569]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2013 |
| 推荐引用方式 GB/T 7714 | Zhao Haifeng,Cheng Jun,Jiang Jun,et al. Multiple instance learning via distance metric optimization[C]. 见:2013 20th IEEE International Conference on Image Processing, ICIP 2013. Melbourne, VIC, Australia. |
入库方式： OAI收割
来源：深圳先进技术研究院
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