2013 Fiscal Year Final Research Report
Learning theory for higher-knowledge self-organization from experiences
| Project/Area Number |
23500280
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
FURUKAWA Tetsuo 九州工業大学, 生命体工学研究科(研究院), 教授 (50219101)
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| Project Period (FY) |
2011 – 2013
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| Keywords | ニューラルネット / 自己組織化写像 / 高次知識獲得 / 学習理論 / 多様体 / テンソル / 自己言及 |
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| Research Abstract |
The purpose of this work is to establish the learning theory and the algorithm acquiring the general knowledge through experiences. The proposed method has a hierarchical structure consisting of a set of first learners and the second learner, in which the first learners estimate models of individual experiences and the second learner leans the learning result of the first learners. We found that the task is described as tensor equations, which can be solved by the higher-rank of self-organizing map (SOM). Furthermore, we developed tensor SOM based on the higher-rank of SOM.
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