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Multiple Instance Learning with Correlated Features
Huang, Yiheng; Zhang, Wensheng; Wang, Jue
AbstractMultiple-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.
KeywordCorrelation Feature Multiple Instance Learning Pairwise Partners Unsupervised Learning
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence
WOS IDWOS:000306825100015
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
AffiliationChinese Acad Sci, Inst Automat, Dept Key Lab Complex Syst Intelligence Sci, Beijing 100190, Peoples R China
Recommended Citation
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.
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