| Project/Area Number |
18500174
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Kobe University |
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Principal Investigator |
OZAWA Seiichi Kobe University, Graduate School of Engneering, Associate Professor (70214129)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥4,110,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥510,000)
Fiscal Year 2007: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2006: ¥1,900,000 (Direct Cost: ¥1,900,000)
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| Keywords | Machine Learning / Multitask Learning / Neural Networks / Incremental Learning / Pattern Recognition / Principal Component Analysis / Features Extraction / Kernel Method |
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| Research Abstract |
This research project developed a new learning algorithm for the multi-task pattern recognition problem. This project considers learning multiple classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is “online" where training examples for different tasks are mixed in a random fashion and given sequentially one after another. It is assumed that the classification tasks are related to each other and that their training examples appear in random sequences during “online training." Thus, the learning algorithm has to continually switch from learning one task to another whenever the training examples change to a different task. This also implies that the learning algorithm has to detect task changes automatically and fast and utilize knowledge of previous tasks to learn new tasks. Overall, automated task recognition falls in the category of unsupervised learning since no information about task categories of training examples is provided to the algorithm. The performance of the algorithm is evaluated using several artificially generated and three UCI datasets. The experiments in this project verify that the proposed algorithm can indeed acquire and accumulate task knowledge and that the transfer of knowledge from tasks already learned enhances the speed of knowledge acquisition on new tasks.
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