{"created":"2025-01-19T01:29:49.032048+00:00","updated":"2025-01-19T11:14:52.806606+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230201","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230201","title":["混合余事象分布と隠れマルコフモデルを内包する未学習クラス推定確率リカレントニューラルネット"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"7b581414-f84e-4f8c-a927-bbea500fecec"},"_deposit":{"id":"230201","pid":{"type":"depid","value":"230201","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"混合余事象分布と隠れマルコフモデルを内包する未学習クラス推定確率リカレントニューラルネット","author_link":["619337","619338","619339"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"混合余事象分布と隠れマルコフモデルを内包する未学習クラス推定確率リカレントニューラルネット"}]},"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":"横浜国大"},{"subitem_text_value":"神奈川県立産業技術総合研究所"},{"subitem_text_value":"横浜国大"}]},"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/230201/files/IPSJ-Z85-5W-02.pdf","label":"IPSJ-Z85-5W-02.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-5W-02.pdf","filesize":[{"value":"528.6 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"91723228-8e18-444a-a277-99ca6f9c92b3","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":"小宮山, 翼"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"迎田, 隆幸"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"島, 圭介"}],"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":"生体信号などの時系列データの分類においてはデータの時間変化を考慮することで分類精度向上が期待されるが，予め設定した識別対象クラスに含まれない異常なパターンを必然的に誤識別してしまうという構造的問題がある．本論文では学習時に想定しない未学習クラスに対する事後確率を推定可能な確率リカレントニューラルネットを提案する．提案法は混合正規分布と異常値の分布を仮定した混合余事象分布を有する隠れマルコフモデルを内包し，時間変化を考慮した多クラス識別と未学習クラス推定および内包する確率モデルのパラメータの学習的獲得を可能とする．人工データと生体信号を用いた時系列データ分類実験において提案法の有効性を確認した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"832","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"831","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230201,"links":{}}