{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00101199","sets":["1164:4179:7434:7569"]},"path":["7569"],"owner":"11","recid":"101199","title":["条件付きロジスティック分布を用いた重み付き多タスク学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2014-05-15"},"_buckets":{"deposit":"b5d23ec9-ca6e-4de6-a354-8efebe69a108"},"_deposit":{"id":"101199","pid":{"type":"depid","value":"101199","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"条件付きロジスティック分布を用いた重み付き多タスク学習","author_link":["0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"条件付きロジスティック分布を用いた重み付き多タスク学習"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"自然言語解析","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2014-05-15","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"奈良先端科学技術大学院大学"},{"subitem_text_value":"奈良先端科学技術大学院大学"},{"subitem_text_value":"奈良先端科学技術大学院大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nara Institute of Sience and Technology","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Sience and Technology","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Sience and 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/101199/files/IPSJ-NL14216010.pdf"},"date":[{"dateType":"Available","dateValue":"2016-05-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-NL14216010.pdf","filesize":[{"value":"815.3 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":"23"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"1f48c7b6-4274-45d9-8d7d-16d6b51893b9","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2014 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"濱口拓男"},{"creatorName":"新保仁"},{"creatorName":"松本裕二"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10115061","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_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"NLP における多くの問題は，クラス分類として定式化される．Multi-Task Feature Learning(MTFL) は，クラス分類や回帰問題といったタスクを複数同時に学習することで，タスク全体の精度を改善する多タスク学習の一種である．しかし MTFL は全てのデータがどれか 1 つのタスクに所属している事を仮定しており，データがどのタスクに所属するかが不明瞭な場合や，複数のタスクに所属している場合には適用できなかった．本論文では，条件付きロジスティック分布という考えを用いることで，そのような状況でも MTFL を適用できる拡張手法を提案する．我々の方法はタスクの情報が無い場合でも，元々の MTFL の精度とほぼ同等の精度を実現する．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告自然言語処理（NL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2014-05-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"2014-NL-216"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"updated":"2025-01-21T11:21:28.276570+00:00","created":"2025-01-18T23:46:50.528654+00:00","id":101199}