Online multiple instance regression
The multiple instance regression problem has become a hot research topic recently. There are several approaches to the multiple instance regression problem, such as Salience, Citation KNN, and MI-ClusterRegress. All of these solutions work in batch mode during the training step. However, in practice...
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| Published in | Chinese physics B Vol. 22; no. 9; pp. 656 - 661 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
01.09.2013
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The multiple instance regression problem has become a hot research topic recently. There are several approaches to the multiple instance regression problem, such as Salience, Citation KNN, and MI-ClusterRegress. All of these solutions work in batch mode during the training step. However, in practice, examples usually arrive in sequence. Therefore, the training step cannot be accomplished once. In this paper, an online multiple instance regression method ＂OnlineMIR＂ is proposed. OnlineMIR can not only predict the label of a new bag, but also update the current regression model with the latest arrived bag. The experimental results show that OnlineMIR achieves good performances on both synthetic and real data sets. |
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| AbstractList | The multiple instance regression problem has become a hot research topic recently. There are several approaches to the multiple instance regression problem, such as Salience, Citation KNN, and MI-ClusterRegress. All of these solutions work in batch mode during the training step. However, in practice, examples usually arrive in sequence. Therefore, the training step cannot be accomplished once. In this paper, an online multiple instance regression method "OnlineMIR" is proposed. OnlineMIR can not only predict the label of a new bag, but also update the current regression model with the latest arrived bag. The experimental results show that OnlineMIR achieves good performances on both synthetic and real data sets. The multiple instance regression problem has become a hot research topic recently. There are several approaches to the multiple instance regression problem, such as Salience, Citation KNN, and MI-ClusterRegress. All of these solutions work in batch mode during the training step. However, in practice, examples usually arrive in sequence. Therefore, the training step cannot be accomplished once. In this paper, an online multiple instance regression method ＂OnlineMIR＂ is proposed. OnlineMIR can not only predict the label of a new bag, but also update the current regression model with the latest arrived bag. The experimental results show that OnlineMIR achieves good performances on both synthetic and real data sets. |
| Author | 王志岗 赵增顺 张长水 |
| AuthorAffiliation | Department of Automation, Tsinghua University, State Key Laboratory of Intelligent Technologie and Systems, Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, China School of Control Science and Engineering, Shandong University, Jinan 250061, China |
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| Cites_doi | 10.1016/S0004-3702(96)00034-3 10.1109/TPAMI.2006.248 10.1016/j.rse.2007.05.017 10.1088/1674-1056/19/11/110502 10.1109/TPAMI.2010.226 10.1088/1674-1056/18/6/014 |
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| Notes | The multiple instance regression problem has become a hot research topic recently. There are several approaches to the multiple instance regression problem, such as Salience, Citation KNN, and MI-ClusterRegress. All of these solutions work in batch mode during the training step. However, in practice, examples usually arrive in sequence. Therefore, the training step cannot be accomplished once. In this paper, an online multiple instance regression method ＂OnlineMIR＂ is proposed. OnlineMIR can not only predict the label of a new bag, but also update the current regression model with the latest arrived bag. The experimental results show that OnlineMIR achieves good performances on both synthetic and real data sets. mutiple instance, regression, online learning 11-5639/O4 Wang Zhi-Gang, Zhao Zeng-Shun, and Zhang Chang-Shui（ a） Department of Automation, Tsinghua University, State Key Laboratory of Intelligent Technologie and Systems, Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China b） College of Information and Electrical Engineenng, Shandong University of Science and Technology, Qingdao 266590, China c） School of Control Science and Engineering, Shandong University, Jinan 250061, China ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
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| References | Zhou Z H (7) 2009 19 Dooly D R (13) 2003; 3 Cauwenberghs G (18) 2001 Fung G (6) 2007 Zeisl B (10) 1 Ray S (2) 2001 Parrella F (17) 2007 Chen Y X (4) 2004; 5 Sun Z H (16) 2010; 19 Andrews S (3) 2003 5 Wagstaff K L (14) 2007 8 Wagstaff K L (15) 2008 Li M (9) 2010 Zhou Z H (11) 2010; 2 Meng Q F (20) 2009; 18 Amar R A (12) 2001 |
| References_xml | – start-page: 291 year: 2008 ident: 15 – start-page: 444 year: 2007 ident: 14 – start-page: 425 year: 2001 ident: 2 – volume: 5 start-page: 939 year: 2004 ident: 4 publication-title: J. Mach. Learn. Res. – start-page: 409 year: 2001 ident: 18 – start-page: 1879 ident: 10 – ident: 1 doi: 10.1016/S0004-3702(96)00034-3 – volume: 2 start-page: 2 issn: 0165-1684 year: 2010 ident: 11 publication-title: Signal Process – ident: 5 doi: 10.1109/TPAMI.2006.248 – year: 2007 ident: 17 – start-page: 3 year: 2001 ident: 12 – ident: 19 doi: 10.1016/j.rse.2007.05.017 – volume: 19 start-page: 110502 issn: 1674-1056 year: 2010 ident: 16 publication-title: Chin. Phys. doi: 10.1088/1674-1056/19/11/110502 – start-page: 577 year: 2003 ident: 3 – start-page: 425 year: 2007 ident: 6 – volume: 3 start-page: 651 year: 2003 ident: 13 publication-title: J. Mach. Learn. Res. – start-page: 1395 year: 2010 ident: 9 – start-page: 1249 year: 2009 ident: 7 – ident: 8 doi: 10.1109/TPAMI.2010.226 – volume: 18 start-page: 2194 issn: 1674-1056 year: 2009 ident: 20 publication-title: Chin. Phys. doi: 10.1088/1674-1056/18/6/014 |
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| SubjectTerms | KNN Labels Mathematical models Online Regression Training 回归模型 回归法 回归问题 在线 多实例 批处理 数据集 |
| Title | Online multiple instance regression |
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