Probabilistic data mining theory using item response theory based on Markov random field
| Project/Area Number |
21700247
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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 |
Sensitivity informatics/Soft computing
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| Research Institution | Tohoku University |
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Principal Investigator |
YASUDA Muneki 東北大学, 大学院・情報科学研究科, 助教 (20532774)
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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 |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2011: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2010: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2009: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 確率的情報処理 / 統計的機械学習理論 / 情報統計力学 / 項目応答理論 / データマイニング / 統計的近似計算理論 / アルゴリズム / 統計的学習理論 |
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| Research Abstract |
An item response theory (IRT)is a recent statistical test theory which has been mainly developed in social science and psychology. In conventional models of IRT, each item has been statistically independent of each other. In this research program, I have proposed a new probabilistic model of an IRT including correlations among items, and have proposed approximate techniques and machine learning algorithms for the proposed model. Since the model is mathematically equivalent to Boltzmann machines which are well known in the area of neural networks and the area of machine learning, the proposed methods can be applied to not only the IRT but also applications in those areas.
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Report
(4 results)
Research Products
(35 results)
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[Presentation] 圧縮センシングによる画像補修2012
Author(s)
片岡駿, 安田宗樹, 樺島祥介, 田中和之
Organizer
情報統計力学の最前線(YSM-SPIP2012)
Place of Presentation
京都大学(招待講演)
Year and Date
2012-03-21
Related Report
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