{"created":"2025-01-19T01:36:46.090582+00:00","updated":"2025-01-19T09:40:50.655013+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00234888","sets":["1164:2735:11468:11669"]},"path":["11669"],"owner":"44499","recid":"234888","title":["アルファベータダイバージェンスに基づく外れ値の割合推定離散分布の場合"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-13"},"_buckets":{"deposit":"6db6c253-b70f-49e6-9430-ae29a1caf10d"},"_deposit":{"id":"234888","pid":{"type":"depid","value":"234888","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"アルファベータダイバージェンスに基づく外れ値の割合推定離散分布の場合","author_link":["640907","640906"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"アルファベータダイバージェンスに基づく外れ値の割合推定離散分布の場合"},{"subitem_title":"Estimating Proportion of Outliers Based on Alpha–Beta Divergence In the Case of Discrete Distributions","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"機械学習・一般","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-06-13","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"豊橋技術科学大学情報メディア基盤センター"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Information and Media Center, Toyohashi 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/234888/files/IPSJ-MPS24148001.pdf","label":"IPSJ-MPS24148001.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS24148001.pdf","filesize":[{"value":"921.3 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"983d134d-e906-46dd-8ce4-5b0429e36053","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"小林, 真佐大"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masahiro, Kobayashi","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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"データ中に混入する外れ値の悪影響を避けるために，ダイバージェンスに基づく統計的推論が研究されている．特によく利用されるダイバージェンスとして，????  ダイバージェンスや  ????  ダイバージェンスなどの密度のべき乗を利用したダイバージェンスが挙げられる．既存研究では，????  ダイバージェンスを用いて，データ分布に最も近い非正規化モデルを推定することにより，モデルパラメータと外れ値の割合を同時推定可能であることが示されている．本研究では，????  ダイバージェンスの一般化である ???????? ダイバージェンスに着目する．上記の結果を拡張し，???????? ダイバージェンスに基づいて推定した場合に，外れ値の割合が同時推定可能であることを示す．また，離散分布に関する数値実験を通して，このことを確認する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"To avoid the adverse effects of outliers mixed in the data, statistical inference based on divergences has been studied. Particularly well-used divergences include those using the density power, such as the ????-divergence and the ????-divergence. Previous research has shown that by using ????-divergence, it is possible to simultaneously estimate the model parameters and the proportion of outliers by estimating the unnormalized model closest to the data distribution. This study focuses on the ????????-divergence, which is a generalization of the ????-divergence. We extend the above results and show that when estimation is based on the ????????-divergence, the proportion of outliers can be simultaneously estimated. Furthermore, we confirm this through numerical experiments on discrete distribution. ","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024-MPS-148"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":234888,"links":{}}