{"updated":"2025-01-20T13:17:03.513649+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00157635","sets":["1164:4402:8598:8599"]},"path":["8599"],"owner":"11","recid":"157635","title":["ベイジアンネットの段階的構造学習法に対する確率的枝刈りを用いた高速化について"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-02-24"},"_buckets":{"deposit":"96def839-db4c-4ff4-a5fb-7c7d34e51a3e"},"_deposit":{"id":"157635","pid":{"type":"depid","value":"157635","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"ベイジアンネットの段階的構造学習法に対する確率的枝刈りを用いた高速化について","author_link":["298449","298446","298444","298445","298448","298447"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ベイジアンネットの段階的構造学習法に対する確率的枝刈りを用いた高速化について"},{"subitem_title":"Improving Learning Speed in Stepwise Structure Learning Method for Bayesian Networks by using Probabilistic Pruning","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2016-02-24","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京工業高等専門学校"},{"subitem_text_value":"東京工業高等専門学校"},{"subitem_text_value":"東京工業高等専門学校"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"National Institute of Technology, Tokyo College","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Technology, Tokyo College","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Technology, Tokyo College","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/157635/files/IPSJ-ICS16182002.pdf","label":"IPSJ-ICS16182002.pdf"},"date":[{"dateType":"Available","dateValue":"2018-02-24"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ICS16182002.pdf","filesize":[{"value":"1.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"25"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"61f9bd8b-5e54-485c-ac37-f55e465aaeb8","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2016 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"北越, 大輔"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"東, 悟大"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鈴木, 雅人"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Daisuke, Kitakoshi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Godai, Azuma","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masato, Suzuki","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11135936","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":"2188-885X","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ベイジアンネット （Bayesian Network：BN） は確率変数間の依存関係を表現した確率モデルとして知られる．本稿では，BN の適切な結合構造を高速に獲得可能な，確率的枝刈りを用いた段階的構造学習法に注目し，その構造学習効率のさらなる改善を図る．段階的構造学習法ではノードをクラスタに分割し，クラスタ間学習を段階的に行うことで探索空間を抑制する．確率的枝刈りは，クラスタ間類似度を基準に学習対象を限定することで構造学習のさらなる高速化を図るが，不適切なクラスタの枝刈りによって，構造の妥当性を大きく損なう可能性がある．本稿では，複数種類の多変量データを対象とした計算機実験を通して，確率的枝刈りを適切に実施可能な設定について検討する．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告知能システム（ICS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2016-02-24","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2016-ICS-182"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:31:31.088685+00:00","id":157635,"links":{}}