2024-03-28T19:03:09Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/473372022-11-17T02:08:08Zhdl_2115_20053hdl_2115_145Propositionalizing the EM algorithm by BDDsBDD上の命題化計算に基づくEMアルゴリズムIshihata, MasakazuKameya, YoshitakaSato, TaisukeMinato, Shin-ichmachine learningEM algorithmbinary decision diagram (BDD)propositonalized probability computation007We 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.人工知能学会Journal Articleapplication/pdfhttp://hdl.handle.net/2115/47337https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/47337/1/18_25_475.pdf1346-07141346-8030Transactions of the Japanese Society for Artificial Intelligence2534754842010jpninfo:doi/10.1527/tjsai.25.475publisher