{"id":229859,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00229859","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"229859","title":["正則化を用いた最大ベイズ境界性学習法について"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"3bc3a00a-5a79-4068-a0d2-af00104b4ebc"},"_deposit":{"id":"229859","pid":{"type":"depid","value":"229859","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"正則化を用いた最大ベイズ境界性学習法について","author_link":["618332","618331","618333"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"正則化を用いた最大ベイズ境界性学習法について"}]},"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":"2023-02-16","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":"同志社大"},{"subitem_text_value":"同志社大"},{"subitem_text_value":"同志社大"}]},"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/229859/files/IPSJ-Z85-2P-04.pdf","label":"IPSJ-Z85-2P-04.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-2P-04.pdf","filesize":[{"value":"904.3 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"ae5c458e-b3d0-4d74-8c43-8dd210a85304","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松重, 仁"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"片桐, 滋"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大崎, 美穂"}],"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":"パターン認識における究極の目標は，最小分類誤り率(ベイズ誤り)状態を達成する分類器パラメータの設定である．損失最小化を経て直接的に分類器のベイズ境界性を高める新しい識別学習法，最大ベイズ境界性(MBB: Maximum Bayes Boundary-ness)学習法が提案された．入力標本ごとにベイズ境界性尺度を定義することで，学習ステップ段階で評価でき，分類器パラメータを最適化することで直接的にベイズ境界の達成を目指す．MBB学習法の評価は必ずしも十分に行われていない．標本数が少ない時や標本が高次元で表される時に，学習において過学習が起きている可能性がある．本論文では，以上のような問題点を解決するために分類器の学習に正則化を組み込み過学習の抑制を目指す．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"118","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"117","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T11:23:18.891531+00:00","created":"2025-01-19T01:29:16.456625+00:00","links":{}}