| Project/Area Number |
15H06823
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Single-year Grants |
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| Research Field |
Statistical science
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
Liu Song 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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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2016: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Keywords | Density Ratio Estimation / Transfer Learning / Markov Network / Graphical Model / Artificial Intelligence / Machine Learning / 統計数学 / 人工知能 / Posterior Ratio / Supervised Learning |
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| Outline of Final Research Achievements |
We have two major achievements throughout the period of this research. Additionally, this project has inspired us to solve another important related problem.
(1) We successfully developed a posterior ratio estimator and it was shown excellent performance on either synthetic or real dataset.(2) A generic theoretical analysis was made for density ratio estimation problems. We investigated the theoretical property of the density ratio estimator for general problems. (3) (Additional) A novel method was proposed for discovering the sparse structure of a partial Graphical Model.
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