2016 Fiscal Year Annual Research Report
| Project/Area Number |
15H06823
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
柳 松 統計数理研究所, 統計的機械学習研究センター, 特任助教 (80760579)
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| Project Period (FY) |
2015-08-28 – 2017-03-31
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| Keywords | Artificial Intelligence / Machine Learning / Transfer Learning / Density Ratio Estimation |
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| Outline of Annual Research Achievements |
Our project "onsite transfer learning (現地の転移学習)" finishes this year. In summary, all research activities under this grant has been conducted as it was planned, and most of the research targets have been met. Moreover, this research project has inspired us with new ideas that we believe are important and should be investigated in near future.
Specifically, We have 1) obtained an effective estimator for conditional density ratio, which can be solved efficiently without using expensive computational resources. 2) We have shown in our experiments, in many tasks, such conditional density ratio estimator can help us transfer a pre-trained classifier given limited number of samples. 3) This estimator is shown to be theoretically consistent under mild conditions.
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| Research Progress Status |
28年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
28年度が最終年度であるため、記入しない。
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