2017 Fiscal Year Final Research Report
Graph-based Information Theoretic Semi-Supervised Learning
Project/Area Number |
15K00307
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Intelligent informatics
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Research Institution | Nara Women's University |
Principal Investigator |
Yoshida Tetsuya 奈良女子大学, 生活環境科学系, 教授 (80294164)
|
Co-Investigator(Renkei-kenkyūsha) |
IMAI Hideyuki 北海道大学, 情報科学研究科, 教授 (10213216)
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Project Period (FY) |
2015-04-01 – 2018-03-31
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Keywords | 情報工学 / 機械学習 / 半教師あり学習 |
Outline of Final Research Achievements |
In order to cope with increasing quantity and variety of data, it is important to develop information technology which enables effective use of domain knowledge. We have developed a graph-based information theoretic semi-supervised learning method. In the developed method, the relationship among data is represented as a graph based on mutual information, and domain knowledge is regarded as constraints and used for regularization. Under the framework of optimization learning, we have developed a semi-supervised learning algorith based on the representation matrix of the graph. The algorithm has been implemented as a prototype system, and experiments over the prototype system were conducted over several benchmark datasets. The results indicate the effectiveness of the developed learning method.
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Free Research Field |
知能情報学
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