2010 Fiscal Year Final Research Report
Development of Learning Theory based on Information Measure
| Project/Area Number |
19300051
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Kyushu University |
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Principal Investigator |
TAKEUCHI Junichi Kyushu University, 大学院・システム情報科学研究院, 教授 (80432871)
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| Co-Investigator(Kenkyū-buntansha) |
TAKAHASHI Norikazu 九州大学, 大学院・システム情報科学研究院, 准教授 (60284551)
JITSUMATSU Yutaka 九州大学, 大学院・システム情報科学研究院, 准教授 (60336063)
川端 勉 電気通信大学, 電気通信学部, 教授 (50152997)
川喜田 雅則 九州大学, 大学院・システム情報科学研究院, 助教 (90435496)
香田 徹 九州大学, 大学院・システム情報科学研究院, 特任教授 (20038102)
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| Project Period (FY) |
2007 – 2010
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| Keywords | 学習理論 / 情報量 / 情報幾何 / 機械学習 |
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| Research Abstract |
We studied machine learning, information theory, and other related topics from a unified viewpoint of minimum description length principle (MDL principle). In particular, we obtained the new sight on the relation between geometrical structure of tree models and stochastic complexity (SC) and that between communication channel capacity and SC. We also studied ensemble learning and kernel method, and obtained efficient learning method for them. Further, on the basis of the fundamental knowledge obtained in this research, we proposed new learning based methods for incident detection in network security, universal portfolio, super resolution etc., and showed their efficiency.
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