| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| 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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