{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00225254","sets":["1164:5352:11207:11208"]},"path":["11208"],"owner":"44499","recid":"225254","title":["LSMR法の停止条件を深層学習する際のデータ数削減方法に関する提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-03-02"},"_buckets":{"deposit":"6c2d733a-291a-46f0-8c7a-b83ed6512f40"},"_deposit":{"id":"225254","pid":{"type":"depid","value":"225254","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"LSMR法の停止条件を深層学習する際のデータ数削減方法に関する提案","author_link":["595679","595675","595682","595680","595676","595677","595678","595681"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"LSMR法の停止条件を深層学習する際のデータ数削減方法に関する提案"},{"subitem_title":"Proposal on how to reduce the number of data when implementing deep learning of stop conditions for the LSMR method","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2023-03-02","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"奈良女子大学"},{"subitem_text_value":"福井大学"},{"subitem_text_value":"福井大学"},{"subitem_text_value":"福井大学"},{"subitem_text_value":"福井大学"},{"subitem_text_value":"奈良女子大学"},{"subitem_text_value":"福井大学"},{"subitem_text_value":"大阪成蹊大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nara Women's University","subitem_text_language":"en"},{"subitem_text_value":"University of Fukui","subitem_text_language":"en"},{"subitem_text_value":"University of Fukui","subitem_text_language":"en"},{"subitem_text_value":"University of Fukui","subitem_text_language":"en"},{"subitem_text_value":"University of Fukui","subitem_text_language":"en"},{"subitem_text_value":"Nara Women's University","subitem_text_language":"en"},{"subitem_text_value":"University of Fukui","subitem_text_language":"en"},{"subitem_text_value":"Osaka Seikei University","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/225254/files/IPSJ-BIO23073031.pdf","label":"IPSJ-BIO23073031.pdf"},"date":[{"dateType":"Available","dateValue":"2025-03-02"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO23073031.pdf","filesize":[{"value":"938.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"295f82cd-73b4-4e70-bda6-99be50219686","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"千代延, 未帆"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"久保井, 五貴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田中, 利佳"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小澤, 伸也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"細田, 陽介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"髙田, 雅美"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"木村, 欣司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 佳正"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","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":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"今日の画像処理技術において，ぼやけ画像の鮮明化は不良条件の連立一次方程式問題である．大次元疎行列を係数に持つ不良条件の線形方程式に対して，反復解法の一つである LSMR 法を適用する．反復解法を用いる場合，ノイズの影響を少なくするために，適切な回数で反復計算を停止する必要がある．反復停止則として機能する2重対角行列の条件数の計算には，特異値計算のための DQDS 法を採用する．本研究報告では，ぼやけ画像と最適な組のデータ，ぼやけ画像行列に特異値分解を適用し，規格化した特異値のデータ，両者のデータをそれぞれ利用した，機械学習に関する性能の比較を行う．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-03-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"31","bibliographicVolumeNumber":"2023-BIO-73"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T12:51:56.456607+00:00","created":"2025-01-19T01:24:45.972425+00:00","id":225254}