| Project/Area Number |
21700304
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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 | Nara Institute of Science and Technology |
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Principal Investigator |
TAKENOUCHI Takashi Nara Institute of Science and Technology, 情報科学研究科, 助教 (50403340)
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| Project Period (FY) |
2009 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2010: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2009: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | パターン認識 / アンサンブル学習 / ECOC / 判別 / 情報統合 / 統計的学習理論 / 判別分析 / ブートストラップ / ブースティング |
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| Research Abstract |
In this program, we developed and analyzed ensemble learning algorithms. Especially, we focused on multi-class classification based on the framework of ECOC, ordered label classification and rating data analysis. For the multi-class classification based on ECOC, we tackled a problem of conventional Bradley-Terry model based method and proposed a novel method, which outperforms the conventional method with drastically lower computational cost. For the ordered label classification, we proposed a Boosting algorithm which can maximize AUC and analyzed its statistical properties. For the rating data, we extended the matrix factorization method using a mixture model and showed effectiveness of the proposed method using a large scale real dataset.
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