| Project/Area Number |
18500124
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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 |
Perception information processing/Intelligent robotics
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| Research Institution | Hokkaido University |
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Principal Investigator |
TOYAMA Jun Hokkaido University, Grad. School of Information Science and Technology, Assist.Prof (60197960)
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| Co-Investigator(Kenkyū-buntansha) |
KUDO Mineichi Hokkaido University, Grad. School of Infornmation Science and Technology, Professor (60205101)
NAKAMURA ATSUYOSHI Hokkaido University, Grad. School of Information Science and Technology, Associate Professor (50344487)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,320,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥420,000)
Fiscal Year 2007: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2006: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Pattern Recognition / Prototype / Feature Selection / Granularity / SDensitivity / Rule extraction / Speech Recognition / 木構造 / 概念 / 識別子独立 |
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| Research Abstract |
1) Classification of Features: The knowledge that is necessary for judgments are not numerical score like a real number but rough categories. A new concept "granularity" based on the rough categories was introduced. We interpreted discrete data as a grouping problem. Therefore an algorithm for discovering semi-optical answer for a grouping problem was proposed.
2) Discovering Prototype: The features that is necessary for judgments are not all knowledge and experiments but some abstract data. From this point of view, an algorithm that discover typical prototype in enormous data was proposed. The prototype is not a point but a object has volume. A prototype update algorithm was also proposed to adapt increasing data with progress of time. On the other hand, we defined the similarity of tree structures. We proposed an algorithm that extracts some typical trees in many trees using the similarity.
3) Feature Selection depend on Individual situations: A classifier-independent feature selection method was proposed. On the other hand, a feature selection method that extract optimum features in random selected features was also proposed from a point of view that optimum feature sets are depend on category and/or category set.
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