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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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2012: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2011: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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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 / 自己組織化マップ / テンソル解析 / 位相保存写像 / 形状空間法 / 関係データ / 推薦システム / 知能アルゴリズム / SOM / テンソル分解 / ニューラルネットワーク / 機械学習 / 自己組織化 |
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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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Report
(4 results)
Research Products
(33 results)
