Multiple Instance Learning with Correlated Features
文献类型：期刊论文
| 作者 | Huang, Yiheng; Zhang, Wensheng ; Wang, Jue
|
| 刊名 | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
 |
| 出版日期 | 2012-06-01 |
| 卷号 | 14期号:2页码:305-313 |
| 关键词 | Correlation feature Multiple instance learning Pairwise partners Unsupervised learning |
| 英文摘要 | Multiple-instance learning (MIL) has received increasing amount of research interest in machine learning recent years for its wide applications in image classification, text categorization, computer security, etc. Unlike supervised learning, in MIL, only the labels of bags are known, the instance labels in positive bags are not available. Many algorithms make the assumption that the instances in the bags are i.i.d samples, but this may not true in practical applications. In this paper, we treat the negative instances in the positive bag as pairwise partners of the positive instances, by using this correlation information, efficient feature is built to describe the bag. Experiment results show that this description is efficient in real world applications. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Automation & Control Systems ; Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Automation & Control Systems ; Computer Science |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000306825100015 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9997]  |
| 专题 | 精密感知与控制研究中心_精密感知与控制 |
| 作者单位 | Chinese Acad Sci, Inst Automat, Dept Key Lab Complex Syst Intelligence Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Yiheng,Zhang, Wensheng,Wang, Jue. Multiple Instance Learning with Correlated Features[J]. INTERNATIONAL JOURNAL OF FUZZY SYSTEMS,2012,14(2):305-313. |
| APA | Huang, Yiheng,Zhang, Wensheng,&Wang, Jue.(2012).Multiple Instance Learning with Correlated Features.INTERNATIONAL JOURNAL OF FUZZY SYSTEMS,14(2),305-313. |
| MLA | Huang, Yiheng,et al."Multiple Instance Learning with Correlated Features".INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 14.2(2012):305-313. |
入库方式： OAI收割
来源：自动化研究所
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