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このセミナーは無事、終了しました。




学習理論の最近の発展を学びたいかたは、下記の論文を読みましょう。

(1) Keisuke Yamazaki. Asymptotic accuracy of Bayes estimation for latent variables with redundancy. Machine Learning (2016) 102: pp.1-28, DOI 10.1007/s10994-015-5482-3.

(2) Keisuke Yamazaki, Daisuke Kaji. Comparing two Bayes methods based on the free energy functions in Bernoulli mixtures. Neural Networks, 44 pp.36-43, 2013.

(3) Miki Aoyagi. Learning coefficient in Bayesian estimation of restricted Boltzmann machine. Journal of Algebraic Statistics, vol. 4, No. 1, pp.30-57, 2013.

(4) Miki Aoyagi, Kenji Nagata. Learning coefficient of generalization error in Bayesian estimation and Vandermonde matrix type singularity. Neural Computation, vol. 24, No. 6, pp.1569 -1610, 2012.

(5) Miki Aoyagi, Sumio Watanabe. Stochastic Complexities of Reduced Rank Regression in Bayesian Estimation, Neural Networks, No. 18, pp.924-933, 2005.

(6) Mathias Drton, Martyn Plummer. A Bayesian information criterion for singular models. J. R. Statist. Soc. B. , Part 2, pp.1-38, 2017.

(7) Kazuho Watanabe. An alternative view of variational Bayes and asymptotic approximations of free energy, Machine Learning, 86(2), 273-293, 2012.

(8) Shinichi Nakajima, Masashi Sugiyama. Theoretical analysis of Bayesian matrix factorization. Journal of Machine Learning Research 12 (Sep), pp.2583-2648, 2011.

(9) Naoki Hayashi, Sumio Watanabe. Upper Bound of Bayesian Generalization Error in Non-Negative Matrix Factorization", Neurocomputing, Vol. 266C, pp.21-28, 2017.

(10) Takeshi Matsuda, Sumio Watanabe. Weighted Blowups of Kullback Information and Application to Multinomial Distributions.2008 International Symposium on Nonlinear Theory and its Applications NOLTA'08, Budapest, Hungary, September pp.7-10, 2008.