中国物理B ›› 2013, Vol. 22 ›› Issue (9): 98702-098702.doi: 10.1088/1674-1056/22/9/098702

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇    下一篇

Online multiple instance regression

王志岗a, 赵增顺b c, 张长水a   

  1. 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 Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
    c School of Control Science and Engineering, Shandong University, Jinan 250061, China
  • 收稿日期:2013-01-16 修回日期:2013-03-08 出版日期:2013-07-26 发布日期:2013-07-26
  • 基金资助:
    Project supported by the China State Key Science and Technology Project on Marine Carbonate Reservoir Characterization (Grant No. 2011ZX05004-003), the National Basic Research Program of China (Grant No. 2013CB329503), the Beijing Municipal Education Commission Science, Technology Development Plan key project, China (Grant No. KZ201210005007), and China Postdoctoral Science Foundation (Grant No. 2012M521336).

Online multiple instance regression

Wang Zhi-Gang (王志岗)a, Zhao Zeng-Shun (赵增顺)b c, Zhang Chang-Shui (张长水)a   

  1. 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 Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
    c School of Control Science and Engineering, Shandong University, Jinan 250061, China
  • Received:2013-01-16 Revised:2013-03-08 Online:2013-07-26 Published:2013-07-26
  • Contact: Zhang Chang-Shui E-mail:zcs@mail.tsinghua.edu.cn
  • Supported by:
    Project supported by the China State Key Science and Technology Project on Marine Carbonate Reservoir Characterization (Grant No. 2011ZX05004-003), the National Basic Research Program of China (Grant No. 2013CB329503), the Beijing Municipal Education Commission Science, Technology Development Plan key project, China (Grant No. KZ201210005007), and China Postdoctoral Science Foundation (Grant No. 2012M521336).

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摘要： 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.

关键词: mutiple instance, regression, online learning

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.

Key words: mutiple instance, regression, online learning

中图分类号:  (Information theory)

  • 87.19.lo
87.15.Qt (Sequence analysis)