{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00234852","sets":["1164:5352:11553:11625"]},"path":["11625"],"owner":"44499","recid":"234852","title":["学習係数を用いたモデル選択sBICの解析およびその応用"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-13"},"_buckets":{"deposit":"5d1622d1-19b8-40c1-a666-af0767394f5c"},"_deposit":{"id":"234852","pid":{"type":"depid","value":"234852","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"学習係数を用いたモデル選択sBICの解析およびその応用","author_link":["640724","640715","640720","640719","640721","640718","640723","640722","640717","640714","640713","640716"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"学習係数を用いたモデル選択sBICの解析およびその応用"},{"subitem_title":"Analysis of the model selection method sBIC with learning coefficients and its applications","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"情報論的学習理論と機械学習3","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-06-13","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日本大学大学院理工学研究科"},{"subitem_text_value":"日本大学大学院理工学研究科"},{"subitem_text_value":"日本大学大学院理工学研究科"},{"subitem_text_value":"日本大学大学院理工学研究科"},{"subitem_text_value":"株式会社デンソーAI 研究部"},{"subitem_text_value":"日本大学大学院理工学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"College of Science & Technology, Nihon University","subitem_text_language":"en"},{"subitem_text_value":"College of Science & Technology, Nihon University","subitem_text_language":"en"},{"subitem_text_value":"College of Science & Technology, Nihon University","subitem_text_language":"en"},{"subitem_text_value":"College of Science & Technology, Nihon University","subitem_text_language":"en"},{"subitem_text_value":"AI R & I Div.,DENSO CORPORATION","subitem_text_language":"en"},{"subitem_text_value":"College of Science & Technology, Nihon University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/234852/files/IPSJ-BIO24078025.pdf","label":"IPSJ-BIO24078025.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO24078025.pdf","filesize":[{"value":"953.2 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"d262dcdf-b0ec-436d-8b30-e6b18b015319","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山崎, 敬太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大場, 智康"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小林, 晴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"清水, 恭介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"梶, 大介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"青柳, 美輝"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Keita, Yamazaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomoyasu, Ohba","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Haru, Kobayashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kyousuke, Shimizu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daisuke, Kaji","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Miki, Aoyagi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年,特異モデルのベイズ学習について,汎化誤差や経験誤差,自由エネルギーなどの漸近挙動を,学習係数やその位数,特異揺らぎを用いて解析できることが証明された [1].理論値は,学習曲線の特徴を表しており,解析や応用に重要な指標である.特に,学習係数は,代数幾何の分野において,log canonical threshold として定義され,汎化誤差の主要項を表す.本研究では,学習係数を用いたモデル選択の手法である特異ベイズ情報量基準 (sBIC) について考察し,その応用として新型コロナウイルス感染症の重症者数推定のモデルを線形ニューラルネットワークとして知られる縮小ランクモデルを使って構築する.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In recent years, it has been demonstrated that the asymptotic behaviors of generalization error, empirical error, and free energy in singular model Bayesian learning can be analyzed using learning coefficients, their orders, and singular fluctuations [1]. The theoretical values represent the characteristics of learning curves and serve as important indicators for analysis and application. Particularly, the learning coefficient is defined as the log canonical threshold in the field of algebraic geometry, serving as an indicator of generalization error. In this study, we examine the Singular Bayesian Information Criterion (sBIC), a method for model selection using learning coefficients. As an application, we construct a model for estimating the number of severe cases of COVID-19 using a reduced-rank model.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学(BIO)"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"25","bibliographicVolumeNumber":"2024-BIO-78"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":234852,"updated":"2025-01-19T09:41:31.589518+00:00","links":{},"created":"2025-01-19T01:36:42.732716+00:00"}