{"created":"2025-01-18T23:01:58.156529+00:00","updated":"2025-01-22T15:54:02.386658+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00033022","sets":["1164:2735:2742:2746"]},"path":["2746"],"owner":"1","recid":"33022","title":["進化的多目的最適化法による ＲＢＦ ネットワークのアンサンブル学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2007-05-17"},"_buckets":{"deposit":"8e55236b-b232-41fe-8e57-80fe7d4293ab"},"_deposit":{"id":"33022","pid":{"type":"depid","value":"33022","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"進化的多目的最適化法による ＲＢＦ ネットワークのアンサンブル学習","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"進化的多目的最適化法による ＲＢＦ ネットワークのアンサンブル学習"},{"subitem_title":"The Ensemble Learning of RBF Networks by Evolutionary Multi-objective Optimization Approach","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2007-05-17","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学大学院情報科学研究科情報数理学専攻"},{"subitem_text_value":"大阪大学大学院情報科学研究科情報数理学専攻"},{"subitem_text_value":"福井工業大学工学部経営情報学科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Information and Physical Sciences, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Department of Information and Physical Sciences, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Department of Management and Information Sciences, Fukui University of Technology","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/33022/files/IPSJ-MPS07064001.pdf"},"date":[{"dateType":"Available","dateValue":"2009-05-17"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS07064001.pdf","filesize":[{"value":"885.6 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":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"058b8c9e-04dc-43b6-b5a5-cc38bbdd3133","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2007 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"近藤, 伸彦"},{"creatorName":"畠中, 利治"},{"creatorName":"魚崎勝司"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Nobuhiko, KONDO","creatorNameLang":"en"},{"creatorName":"Toshiharu, HATANAKA","creatorNameLang":"en"},{"creatorName":"Katsuji, UOSAKI","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_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"RBF ネットワークの構造設計をモデルの複雑さに関する多目的最適化問題ととらえてこれを解くことで、多目的の意味で優れた RBF ネットワークの集合を求めることができる。こうして求められた多様な構造の RBF ネットワーク集合からアンサンブルを構築すれば、汎化能力に優れたモデルを構成できると考えられる。本研究では、進化的多目的最適化による RBF ネットワークの構築法によるアンサンブルを取り上げ、そのパターン分類への適用を検討した。アンサンブルを構成するメンバーの選択法と各メンバーの出力の結合法の組合せに対して、パターン分類のベンチマークテストデータを用いて数値実験を行った結果、従来のニューラルネットワークアンサンブルの手法と同等以上の能力を持つことが示された。","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The structure determination problem of RBF network can be considered as multi-objective optimization problem about model complexity. A set of RBF networks which is multi-objectively optimized can be obtained by solving the above problem. In this paper the construction of RBF networks by evolutionary multi-objective optimization method and its ensemble are considered, and it is applied to the pattern classification problem. In this paper, several combinations of ensemble member selection methods and output combination methods are considered. Experimental study on the benchmark problem of the pattern classification is carried out, then it is illustrated that the RBF network ensemble has comparable performance to the other ensemble methods.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"4","bibliographic_titles":[{"bibliographic_title":"情報処理学会研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2007-05-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"43(2007-MPS-064)","bibliographicVolumeNumber":"2007"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"id":33022,"links":{}}