Online multiple instance regression |
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| Authors: | Wang Zhi-Gang Zhao Zeng-Shun and Zhang Chang-Shui |
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| Institution: | [1]Department of Automation, Tsinghua University, State Key Laboratory of Intelligent Technologie and Systems, Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China [2]College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, China [3]School of Control Science and Engineering, Shandong University, Jinan 250061, China |
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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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| Keywords: | mutiple instance regression online learning |
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