| Project/Area Number |
02680031
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Informatics
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| Research Institution | Kyushu University |
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Principal Investigator |
MIYANO Satoru Kyushu Univ., Fac. Sci., Assoc. Prof., 理学部, 助教授 (50128104)
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| Co-Investigator(Kenkyū-buntansha) |
MIYAHARA Tetsuhiro Kyushu Univ., Fac. Gen. Edu., Assoc. Prof., 教養部, 助教授 (90209932)
SHINOHARA Ayumi Kyushu Univ., Fac. Sci., Assistant, 理学部, 助手 (00226151)
ARIKAWA Setsuo Kyushu Univ., Fac. Sci., Professor, 理学部, 教授 (40037221)
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| Project Period (FY) |
1990 – 1991
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| Project Status |
Completed (Fiscal Year 1991)
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| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 1991: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1990: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | Computational learning / Teaching theory / Analogical reasoning / Machine learning / Parallel algorithm / Decision tree |
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| Research Abstract |
This research concentrates on computational learning from the view point of algorithmic teaching. We introduced the notion of teachability with which we established a relationship between the learnability and teachability. We obtained some results on the complexity issues of a teacher in relation to learning. We also considered the analogical reasoning in relation to learning. We showed that NP-hard aspect appears in analogicaj reasoning. In our framework, the set cover problem proved to be an important issue for finding keys in teaching. For this problem we devised a parallel algorithm which solves the minimal set cover problem. We also investigated elementary formal systems with respect to polynomial-time learnability. We identified some important and useful subclasses of elementary formal systems which are polynomial-time learnable. Based on these theoretical researches, we developed a machine learning system which employs decision trees over regular patterns. This system finds important keys from randomly chosen samples which may explain the given samples reasonably. In addition to theoretical analysis of our system, experiments show that this machine learning system exhibited quite successful results. In order to find keys in teaching, we also developed a system based on elementary formal systems which employs the approximation algorithm for minimum set cover problem. Comparison of these two systems showed that the former is faster and efficient than the latter. But, by theoretical reasons, the latter system can cope with problems with larger variety.
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