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13 1 IDC Wo rldwide Business Analytics Technology and Services Forecast Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, Fairness, Acountability, and Transparency in Machine Learning, 6 Cynthia Rudin, Algorithm for interpretable machine learning, Invited Talk in 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining KDD, Big Data: Seizing Opportunities, Preserving Values, Exective Office of the President White House, 2014, print.pdf 8 Vladimir N. Vapnik, The nature of statistical Learning Theory, Springer-Verlag New York, Akaike, H., Information theory and an extension of the maximum likelihood principle Proceedings of the 2nd International Symposium on Information Theory, , G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 6 2 : , S. Watanabe, Algebraic Geometry and Statistical Learning Theory, Cambridge University Press, M. Lichman, UCI Machine Learning Repository,

14 13 Ryohei Fujimaki, Yasuhiro Sogawa, Satoshi Morinaga: Online heterogeneous mixture modeling with marginal and copula selection. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD, Ryohei Fujimaki, Satoshi Morinaga: Factorized Asymptotic Bayesian Inference for Mixture Modeling. Proceedings of the The fifteenth international conference on Artificial Intelligence and Statistics AISTATS, Ryohei Fujimaki, Kohei Hayashi: Factorized Asymptotic Bayesian Hidden Markov Model. Proceedings of the 25th international conference on machine learning ICML, K. Hayashi and R. Fujimaki, "Factorized Asymptotic Bayesian Inference for Latent Feature Models", 27th Annual Conference on Neural Information Processing Systems NIPS, Riki Eto, Ryohei Fujimaki, Satoshi Morinaga, Hiroshi Tamano, Fully-Automatic Bayesian Piece-wise Sparse Linear Models, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics AISTATS, Ji Liu, Ryohei Fujimaki and Jieping Ye, "Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint", Proceedings of the 27th international conference on machine learning ICML, H. Oiwa and R. Fujimaki, "Partition-wise Linear Models", 28th Annual Conference on Neural Information Processing Systems NIPS, NEC NEC press/201310/ _02.html 21 NEC NEC 23 NEC NEC NEC

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