{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218600","sets":["1164:2735:10865:10962"]},"path":["10962"],"owner":"44499","recid":"218600","title":["経験的誤差最小化に対する帰着スキームとマルチインスタンス学習への応用"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-06-20"},"_buckets":{"deposit":"9b925100-f33b-4677-963b-db9b2e5c3505"},"_deposit":{"id":"218600","pid":{"type":"depid","value":"218600","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"経験的誤差最小化に対する帰着スキームとマルチインスタンス学習への応用","author_link":["568910","568911","568909"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"経験的誤差最小化に対する帰着スキームとマルチインスタンス学習への応用"},{"subitem_title":"Reduction scheme for empirical risk minimization and its applications to Multiple-Instance Learning","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-06-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"九州大学大学院システム情報科学研究院 情報知能工学部門／理化学研究所革新知能統合研究センター"},{"subitem_text_value":"九州大学大学院システム情報科学研究院 情報学部門"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Kyushu University, Department of Advanced Information Technology, Faculty of Information Science and Electrical Engineering/RIKEN, Center for Advanced Intelligence Project","subitem_text_language":"en"},{"subitem_text_value":"Kyushu University, Department of Informatics, Faculty of Information Science and Electrical Engineering","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/218600/files/IPSJ-MPS22138030.pdf","label":"IPSJ-MPS22138030.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS22138030.pdf","filesize":[{"value":"874.0 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"66a9fd07-57fb-494d-a48f-f1a26851d7ed","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 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":"末廣, 大貴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"瀧本, 英二"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Daiki, Suehiro Eiji Takimoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","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":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"In statistical learning, many problem formulations have been proposed so far, such as multi-class learning, complementarily labeled learning, multi-label learning, multi-task learning, which provide theoretical models for various real-world tasks. Although they have been extensively studied, the relationship among them has not been fully investigated. In this work, we focus on a particular problem formulation called Multiple-Instance Learning (MIL), and show that various learning problems including all the problems mentioned above with some of new problems can be reduced to MIL with theoretically guaranteed generalization bounds, where the reductions are established under a new reduction scheme we provide as a by-product. The results imply that the MIL-reduction gives a simplified and unified framework for designing and analyzing algorithms for various learning problems. Moreover, we show that the MIL-reduction framework can be kernelized.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In statistical learning, many problem formulations have been proposed so far, such as multi-class learning, complementarily labeled learning, multi-label learning, multi-task learning, which provide theoretical models for various real-world tasks. Although they have been extensively studied, the relationship among them has not been fully investigated. In this work, we focus on a particular problem formulation called Multiple-Instance Learning (MIL), and show that various learning problems including all the problems mentioned above with some of new problems can be reduced to MIL with theoretically guaranteed generalization bounds, where the reductions are established under a new reduction scheme we provide as a by-product. The results imply that the MIL-reduction gives a simplified and unified framework for designing and analyzing algorithms for various learning problems. Moreover, we show that the MIL-reduction framework can be kernelized.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-06-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"30","bibliographicVolumeNumber":"2022-MPS-138"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T15:06:47.840221+00:00","created":"2025-01-19T01:18:57.347027+00:00","links":{},"id":218600}