| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2013: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Outline of Final Research Achievements |
In order for machine learning to work effectively, it is very important to accurately capture the distance between input signals. In this research project, we tried to improve machine learning algorithm by introducing natural structure into space based on the framework of information geometry and reproducing kernel Hilbert space theory. As applications, the effectiveness was confirmed by applying to the problem of transfer learning which improves learning accuracy by synthesizing the results of similar learning problems when the number of data is small, and the problem of estimating the dimension of low dimensional structure embedded in high dimension space.
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