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Propositionalizing the EM algorithm by BDDs

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Title: Propositionalizing the EM algorithm by BDDs
Other Titles: BDD上の命題化計算に基づくEMアルゴリズム
Authors: Ishihata, Masakazu1 Browse this author
Kameya, Yoshitaka2 Browse this author
Sato, Taisuke3 Browse this author
Minato, Shin-ich4 Browse this author →KAKEN DB
Authors(alt): 石畠, 正和1
亀谷, 由隆2
佐藤, 泰介3
湊, 真一4
Keywords: machine learning
EM algorithm
binary decision diagram (BDD)
propositonalized probability computation
Issue Date: 2010
Publisher: 人工知能学会
Journal Title: Transactions of the Japanese Society for Artificial Intelligence
Journal Title(alt): 人工知能学会論文誌
Volume: 25
Issue: 3
Start Page: 475
End Page: 484
Publisher DOI: 10.1527/tjsai.25.475
Abstract: We propose an Expectation-Maximization (EM) algorithm which works on binary decision diagrams (BDDs). The proposed algorithm, BDD-EM algorithm, opens a way to apply BDDs to statistical learning. The BDD-EM algorithm makes it possible to learn probabilities in statistical models described by Boolean formulas, and the time complexity is proportional to the size of BDDs representing them. We apply the BDD-EM algorithm to prediction of intermittent errors in logic circuits and demonstrate that it can identify error gates in a 3bit adder circuit.
Type: article
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 湊 真一

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