一般注記元資料の権利情報 : 許諾条件により全文は2022-08-10に公開
元資料の権利情報 : This thesis includes the following contents as parts. ・A. Tanimoto. Combinatorial Q-learning for condition-based infrastructure maintenance. IEEE Access, 2021. doi: 10.1109/ACCESS.2021.3059244. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Kyoto University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. ・A. Tanimoto, S. Yamada, T. Takenouchi, M. Sugiyama, and H. Kashima. Improving imbalanced classification using near-miss instances. Expert Systems with Applications, 201:117130, 2022. doi: 10.1016/j.eswa.2022.117130. ・A. Tanimoto, T. Sakai, T. Takenouchi, and H. Kashima. Regret minimiza- tion for causal inference on large treatment space. In International Con- ference on Artificial Intelligence and Statistics (AISTATS), pages 946–954, 2021.
・A. Tanimoto, T. Sakai, T. Takenouchi, and H. Kashima. Causal combinatorial factorization machines for set-wise recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2021. doi: 10.1007/978-3-030-75765-6_40.
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