{"created":"2023-06-20T12:59:58.884886+00:00","id":4052,"links":{},"metadata":{"_buckets":{"deposit":"a05e9fb8-6e02-44ee-a405-e4e702d7acbd"},"_deposit":{"created_by":2,"id":"4052","owners":[2],"pid":{"revision_id":0,"type":"depid","value":"4052"},"status":"published"},"_oai":{"id":"oai:fun.repo.nii.ac.jp:00004052","sets":["22:28","25:81:101"]},"author_link":["7284","95","7285","7286"],"item_5_biblio_info_5":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2007","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"2244","bibliographicPageStart":"2183","bibliographicVolumeNumber":"19","bibliographic_titles":[{"bibliographic_title":"Neural Computation"}]}]},"item_5_description_3":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"一般化されたU-ブーストのアルゴリズムは期待損失関数の逐次最小化として定式化され,アルゴリズムの統計的性質は用いる損失関数によって決定される.本論文では古典的な判別モデルであるロジスティックモデルと関連したクラスの損失関数に着目し,頑健性についての考察を行った.頑健性の指標としてしばしば用いられるgross error sensitivityの意味で最も頑健な損失関数を理論的に明らかにし,その頑健性を数値実験で確かめた.","subitem_description_type":"Abstract"}]},"item_5_publisher_17":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"MIT Press"}]},"item_5_select_10":{"attribute_name":"単著共著","attribute_value_mlt":[{"subitem_select_item":"共著/joint"}]},"item_5_select_8":{"attribute_name":"査読有無","attribute_value_mlt":[{"subitem_select_item":"あり/yes"}]},"item_5_select_9":{"attribute_name":"研究業績種別","attribute_value_mlt":[{"subitem_select_item":"原著論文/Original Paper"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kanamori, Takafumi"}],"nameIdentifiers":[{"nameIdentifier":"7284","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Takenouchi, Takashi"}],"nameIdentifiers":[{"nameIdentifier":"95","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"50403340","nameIdentifierScheme":"e-Rad","nameIdentifierURI":"https://kaken.nii.ac.jp/ja/search/?qm=50403340"}]},{"creatorNames":[{"creatorName":"Eguchi, Shinto"}],"nameIdentifiers":[{"nameIdentifier":"7285","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Murata, Noboru"}],"nameIdentifiers":[{"nameIdentifier":"7286","nameIdentifierScheme":"WEKO"}]}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Robust Loss Functions for Boosting","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Robust Loss Functions for Boosting"}]},"item_type_id":"5","owner":"2","path":["28","101"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-04-15"},"publish_date":"2013-04-15","publish_status":"0","recid":"4052","relation_version_is_last":true,"title":["Robust Loss Functions for Boosting"],"weko_creator_id":"2","weko_shared_id":-1},"updated":"2023-06-20T13:31:55.990962+00:00"}