{"updated":"2025-01-20T11:42:07.726344+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00162560","sets":["6504:8672:8736"]},"path":["8736"],"owner":"6748","recid":"162560","title":["区分線形近似を用いた逐次ロジットモデルの変数選択"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-03-10"},"_buckets":{"deposit":"8fc6f454-d908-461c-a722-65d65cb1d318"},"_deposit":{"id":"162560","pid":{"type":"depid","value":"162560","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"区分線形近似を用いた逐次ロジットモデルの変数選択","author_link":["317280","317281","317279"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"区分線形近似を用いた逐次ロジットモデルの変数選択"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2016-03-10","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"筑波大"},{"subitem_text_value":"専修大"},{"subitem_text_value":"農工大"}]},"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/162560/files/IPSJ-Z78-3C-01.pdf","label":"IPSJ-Z78-3C-01.pdf"},"date":[{"dateType":"Available","dateValue":"2016-05-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-Z78-3C-01.pdf","filesize":[{"value":"127.3 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"7edbe13e-efb2-4c3d-9881-8c25262b04c2","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2016 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"佐藤, 俊樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高野, 祐一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"宮代, 隆平"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では，逐次ロジットモデルの変数選択問題を考える．変数選択とは，候補となる説明変数の集合から，有用な部分集合を選択することである．この問題に対してTanaka and Nakagawa (2014) は，ロジスティック損失関数を二次近似し混合整数二次最適化問題として定式化して解く方法を提案した．しかし，この手法は近似の誤差が大きく，良い変数集合を選ぶことは難しい．そこで本研究では情報量規準に基づく変数選択問題に対して，ロジスティック損失関数を区分線形近似し混合整数線形最適化問題として定式化する手法を提案する．また，二次近似を用いた変数選択手法と性能を比較する数値実験を実施し，提案手法の有効性を検証する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"58","bibliographic_titles":[{"bibliographic_title":"第78回全国大会講演論文集"}],"bibliographicPageStart":"57","bibliographicIssueDates":{"bibliographicIssueDate":"2016-03-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2016"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"created":"2025-01-19T00:34:35.990585+00:00","id":162560,"links":{}}