| Project/Area Number |
07680413
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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 | Science University of Tokyo |
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Principal Investigator |
MIZOGUCHI Fumio Science Univ. of Tokyo, Industrial Administratin Dep Professor, 理工学部, 教授 (50084463)
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| Co-Investigator(Kenkyū-buntansha) |
NAMBA Kazuaki Science Univ.to Tokyo, Indnstrial Administratim Dep Lecturer, 理工学部, 講師 (10228108)
OHWADA Hayato Science Univ.to Tokyo, Industrial Administratim Dep Lecturer, 理工学部, 講師 (30203954)
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| Project Period (FY) |
1995 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 1997: ¥300,000 (Direct Cost: ¥300,000)
Fiscal Year 1996: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 1995: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | CAI system / Internet / WWW browser / Electrical syllabus / Inductive Learning / Automatic classification / 電子ドキュメント / 情報フィルタリング / World wide web / 情報検索 / 個人の嗜好 / ハイパーテキスト / HTML / CAI |
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| Research Abstract |
Computers are becoming the vehicle for an increasing range of everyday activities. WWW browsers and electronic mail systems are typical examples of the fast-growing area of computing.
This research focused on a computer-aided instruction systems based on this recent Internet technology. In particular, we developed an electrical syllabus for our students at the Science University of Tokyo.
To deal with a large volume of WWW pages for the syllabus, we developed an intelligent search engine, SEIL (Search Engine using Inductive Learning) , which finds user-desirable WWW pages without keywords. SEIL works as the following steps.
First, SEIL presents some WWW pages to users and gets their interests interactively. Second, using Inductive Learning, the keywords that a user seems to have interest are derived automatically. Third, SEIL retrieves WWW pages using these keywords like normal engines. These steps makes search engine to detect implicit user's interests. We conducted some experiments where a number of students used the electrical syllabus and SEIL, and their impression was analyzed using our inductive learning system. We reported on this result and provided an insight from this experiment towards practical use of the electrical syllabus based on inductive learning.
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