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| ファイル | 記述 | サイズ | フォーマット | |
|---|---|---|---|---|
| djohk00772.pdf | Dissertation_全文 | 3.81 MB | Adobe PDF | 見る/開く |
| yjohk00772.pdf | Abstract_要旨 | 183.83 kB | Adobe PDF | 見る/開く |
完全メタデータレコード
| DCフィールド | 値 | 言語 |
|---|---|---|
| dc.contributor.author | Tanimoto, Akira | en |
| dc.contributor.alternative | 谷本, 啓 | ja |
| dc.contributor.transcription | タニモト, アキラ | ja-Kana |
| dc.date.accessioned | 2021-11-19T00:27:34Z | - |
| dc.date.available | 2021-11-19T00:27:34Z | - |
| dc.date.issued | 2021-09-24 | - |
| dc.identifier.uri | http://hdl.handle.net/2433/266070 | - |
| dc.language.iso | eng | - |
| dc.publisher | Kyoto University | en |
| dc.publisher.alternative | 京都大学 | ja |
| dc.rights | 許諾条件により全文は2022-08-10に公開 | ja |
| dc.rights | 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. | en |
| dc.subject | Artificial intelligence | en |
| dc.subject | Decision-making support system | en |
| dc.subject | Machine learning | en |
| dc.subject | Causal inference | en |
| dc.subject | Reinforcement learning | en |
| dc.subject | Sample-efficiency | en |
| dc.subject.ndc | 007 | - |
| dc.title | Goal-oriented Modeling for Data-driven Decision Making | en |
| dc.title.alternative | データ駆動型意思決定のための目的指向モデリング | ja |
| dc.type | doctoral thesis | - |
| dc.type.niitype | Thesis or Dissertation | - |
| dc.textversion | ETD | - |
| dc.description.degreegrantor | 京都大学 | ja |
| dc.description.degreelevel | 新制・課程博士 | - |
| dc.description.degreediscipline | 博士(情報学) | ja |
| dc.description.degreereportnumber | 甲第23542号 | - |
| dc.description.degreenumber | 情博第772号 | - |
| dc.description.degreekucallnumber | 新制||情||132(附属図書館) | - |
| dc.date.granted | 2021-09-24 | - |
| dc.description.degreeaffiliation | 京都大学大学院情報学研究科知能情報学専攻 | - |
| dc.description.degreeexamcommittee | (主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 下平 英寿 | - |
| dc.description.degreeprovision | 学位規則第4条第1項該当 | - |
| dc.identifier.selfDOI | 10.14989/doctor.k23542 | - |
| dcterms.accessRights | open access | - |
| datacite.date.available | 2022-08-10 | - |
| dc.description.degreediscipline-en | Doctor of Informatics | en |
| dc.identifier.degreegrantorID | 14301 | - |
| dc.description.degreegrantor-en | Kyoto University | en |
| dc.description.degreeObjectType | DFAM | - |
| jpcoar.contributor.Type | Supervisor | - |
| jpcoar.contributor.Type | Supervisor | - |
| jpcoar.contributor.Type | Supervisor | - |
| jpcoar.contributor.Name | 鹿島, 久嗣 | ja |
| jpcoar.contributor.Name | 山本, 章博 | ja |
| jpcoar.contributor.Name | 下平, 英寿 | ja |
| 出現コレクション: | 140 博士(情報学) | |
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