2011 Fiscal Year Final Research Report
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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| 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 |
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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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