| Budget Amount *help |
¥25,350,000 (Direct Cost: ¥19,500,000、Indirect Cost: ¥5,850,000)
Fiscal Year 2016: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2015: ¥7,930,000 (Direct Cost: ¥6,100,000、Indirect Cost: ¥1,830,000)
Fiscal Year 2014: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2013: ¥8,320,000 (Direct Cost: ¥6,400,000、Indirect Cost: ¥1,920,000)
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| Outline of Final Research Achievements |
In this research project, we developed methods for directly learning the density ratio and density difference without estimating each density, and based on them, we developed various machine learning algorithms. This includes algorithms of semi-supervised classification, unsupervised clustering, supervised causal inference, supervised dimension reduction, unsupervised dimension reduction, classification from positive and unlabeled data, supervised learning under target shift, and cross-domain object matching. We also developed methods for directly learning the density derivative without estimating the density itself, and based on them, we developed algorithms of modal regression and non-Gaussian component analysis.
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