Development of method to analyze the high-dimensional and small-sample data based on machine learning and its application to cryptanalysis
| Project/Area Number |
21700308
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Statistical science
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| Research Institution | Kyushu University |
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Principal Investigator |
KAWAKITA Masanori 九州大学, システム情報科学研究院・情報学部門 (90435496)
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| Project Period (FY) |
2009 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| 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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| Keywords | 統計的学習理論 / n<<p問題 / 変数選択 / 重み付き尤度法 / 半教師付き学習 / 統計的パラドックス / 密度比推定 / n≪p問題 |
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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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Report
(4 results)
Research Products
(23 results)
