Knowledge learning and understanding from incomplete data based on pattern similarity
| Project/Area Number |
19500128
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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 |
Intelligent informatics
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| Research Institution | The University of Aizu |
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Principal Investigator |
ZHAO Qiangfu The University of Aizu, コンピュータ理工学部, 教授 (90260421)
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| Co-Investigator(Kenkyū-buntansha) |
LIU YONG 会津大学, コンピュータ理工学部, 上級準教授 (60325967)
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| Project Period (FY) |
2007 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2009: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2008: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2007: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 学習と発見 / 機械学習 / 学習と理解 / 知識獲得 / 最近傍識別木 / 不完全データ / 特徴抽出 / ファジィ変換 / 次元圧縮 / 特微抽出 / ファジィー変換 |
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| Research Abstract |
To acquire understandable knowledge through machine learning, we have proposed the nearest neighbor classification tree (NNC-Tree) and an induction method. NNC-Tree is a kind of multivariate decision trees based on pattern similarity. In this project, we have proposed (1) a method for selecting important features through learning ; (2) a method for unifying the learning algorithms through data fuzzification ; and (3) a method for efficient dimensionality reduction. With these new contributions, we can induce multivariate decision trees more efficiently.
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Report
(4 results)
Research Products
(28 results)
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[Presentation] Pose Recognition with NNC-Tree2007
Author(s)
Jie Ji, Naoki Tominaga, Akiha Iwase, Yoshihiko Watanabe, Kazuhiko Hirakuri, Qiangfu Zhao
Organizer
電子情報通信学会パターン認識・理解研究会、信学技報Vol.107, No.384, pp.37-42.
Place of Presentation
神戸大学
Year and Date
2007-12-13
Related Report
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