| Budget Amount *help |
¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2011: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2010: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2009: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Research Abstract |
It is already known that the estimation accuracy of supervised learning can be improved by using the unlabeled data even when the number of labeled data is quite small. This type of learning is called semi-supervised learning. The most conventional semi-supervised learning requires some additional assumptions to dominate the supervised learning even though we have additional information. Further, as for model selection, the conventional criteria(including AIC or CV) are applied to the labeled data. However, because such criteria require a large number of labeled data, they do not work well in this setting. Our main result is that we solved these problems.
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