{"id":18021,"updated":"2025-01-22T23:01:03.837467+00:00","links":{},"created":"2025-01-18T22:50:51.948089+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00018021","sets":["934:1085:1101:1102"]},"path":["1102"],"owner":"1","recid":"18021","title":["アンサンブル学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2005-10-15"},"_buckets":{"deposit":"6bb26c86-0418-4bc2-bab8-a22b5bfeb9e9"},"_deposit":{"id":"18021","pid":{"type":"depid","value":"18021","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"アンサンブル学習","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"アンサンブル学習"},{"subitem_title":"Ensemble Learning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"解説論文","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2005-10-15","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日本電信電話株式会社NTTコミュニケーション科学基礎研究所"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation","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/18021/files/IPSJ-TCVIM4615004.pdf"},"date":[{"dateType":"Available","dateValue":"2007-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TCVIM4615004.pdf","filesize":[{"value":"260.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"62d65fcb-1c5b-4a88-9fcc-c98d3698d347","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2005 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"上田, 修功"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Naonori, Ueda","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11560603","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7810","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"パターン識別器の性能は，設計に使用した学習データだけでなく，未知データに対する識別性能（汎化性能）で評価される．換言すれば，汎化性能の高い識別器の学習法の研究が実用上重要となる．その1つの有力なアプローチとしてアンサンブル学習がある．本論文では，アンサンブル学習に関する従来の代表的手法を，その基本的な考え方，具体的なアルゴリズムを含めサーベイを行う．さらに，最新の話題として，異種情報を効果的に融合する従来とは別のタイプのアンサンブル学習についても解説する．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The classification performance of a classifier is evaluated in terms of minimizing the classification errors for unseen test data. In other words, it is crucial to develop methods for learning classifiers with high generalization ability for the test data. Ensemble learning is a promising approach for this purpose. The aim of this paper is to give a review on literature dealing with the ensemble learning. The basic ideas and their learning algorithms of the conventional representative ensemble learning methods are explained. Moreover, another type of ensemble methods that effectively combine heterogeneous information are also included as the recent results.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"20","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"11","bibliographicIssueDates":{"bibliographicIssueDate":"2005-10-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"SIG15(CVIM12)","bibliographicVolumeNumber":"46"}]},"relation_version_is_last":true,"weko_creator_id":"1"}}