| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Outline of Final Research Achievements |
The main important result of this study is that we provided a way to extend Barron and Cover’s theory to supervised learning without any significant lack of its virtues, which had been considered to be difficult. Our extension leads to a risk estimator of supervised learning without conventional assumptions like boundedness of random variables and/or asymptotic assumption. By our method, we succeeded in deriving a new risk bound of the most famous compressed sensing algorithm (lasso). We also extended these results to semi-supervised learning and sparse coding. Furthermore, by implementing semi-supervised sparse coding, we construct a new semi-supervised super-resolution algorithm. We show that the accuracy of super-resolution can be improved by semi-supervised super-resolution by numerical experiments though its extent strongly depends on input images.
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