| Project/Area Number |
13480074
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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 |
計算機科学
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| Research Institution | Tohoku University |
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Principal Investigator |
MARUOKA Akira Graduate School of Information Sciences, Professor, 大学院・情報科学研究科, 教授 (50005427)
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| Co-Investigator(Kenkyū-buntansha) |
AMANO Kazuyuki Graduate School of Information Sciences, Research associate, 大学院・情報科学研究科, 助手 (30282031)
TAKIMOTO Eiji Graduate School of Information Sciences, Associate Professor, 大学院・情報科学研究科, 助教授 (50236395)
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| Project Period (FY) |
2001 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥15,700,000 (Direct Cost: ¥15,700,000)
Fiscal Year 2002: ¥6,300,000 (Direct Cost: ¥6,300,000)
Fiscal Year 2001: ¥9,400,000 (Direct Cost: ¥9,400,000)
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| Keywords | boosting / learning from examples / on-line learning algorithm / prunning / decision tree boosting / generalized entropy / learning curve / over-fitting / マージン / サポートベクトルマシーン / 勾配傾斜法 / ランダムプロジェクション / 次元圧縮 / m-限定独立 |
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| Research Abstract |
The amount of data collected from various fields is growing exponentially and the task of exracting useful information from data is becoming more and more difficult accordingly. To overcome the difficulty that comes from the limitation on computational resources, we investigate various methodologies of exracting useful information from huge data by organizing consice data based on pseudo-entropy function. The results we obtained includes the following algorithms : top-down decision tree learning algorithm based on information based boosting ; an algorithm to obtain the nearly best prunnig of a decision trre ; an algorithm to learn monotone log-term DNF formulas under uniform distribution.
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