{"created":"2021-03-01T06:01:16.102820+00:00","id":8957,"links":{},"metadata":{"_buckets":{"deposit":"b95fc386-d619-44f7-8f45-d8d79ad3ed1a"},"_deposit":{"id":"8957","owners":[],"pid":{"revision_id":0,"type":"depid","value":"8957"},"status":"published"},"_oai":{"id":"oai:kitami-it.repo.nii.ac.jp:00008957","sets":["1:86"]},"author_link":["273","7687"],"item_1646810750418":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_3_alternative_title_198":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"Semi-supervised Learning for a Healthcare Support Method using Markov Decision Processes","subitem_alternative_title_language":"en"}]},"item_3_biblio_info_186":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2019-05","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicPageEnd":"60","bibliographicPageStart":"49","bibliographicVolumeNumber":"21","bibliographic_titles":[{"bibliographic_title":"バイオメディカル・ファジィ・システム学会誌 = Journal of Biomedical Fuzzy Systems Association"}]}]},"item_3_description_184":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"従来研究では，ヘルスケア支援方法を表現するための確率モデルとしてマルコフ決定過程が採用され，マルコフ決定過程の真のパラメータが既知の仮定のもとで検討されている．本研究では，より現実に近い真のパラメータが未知の仮定のもとでヘルスケア支援のための半教師付き学習方法を提案する．学習データは完全データと不完全データによって構成される．提案方法ではEMアルゴリズム(expectation-maximization algorithm) を用いる．数例のシミュレーションによって提案方法の有効性を示す．シミュレーション結果より，学習データが大きくなるにつれて学習精度が高くなることが確認できる．","subitem_description_type":"Abstract"},{"subitem_description":"[ENG]\nMarkov decision processes are applied to a healthcare support method in previous research. In the previous research the true parameters of Markov decision processes are known. In this research we propose a semi-supervised learning method for the healthcare support method under the condition that the true parameters of Markov decision processes are unknown. Learning data consist of complete data and incomplete data. In the proposed method EM(expectation-maximization) algorithm is used. The effectiveness of the proposed method is shown by some simulations. The result shows that the learning accuracy becomes higher as the learning data becomes bigger.","subitem_description_type":"Abstract"}]},"item_3_publisher_212":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"バイオメディカル・ファジィ・システム学会"}]},"item_3_relation_208":{"attribute_name":"論文ID（NAID）","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"40021956557","subitem_relation_type_select":"NAID"}}]},"item_3_rights_192":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"Copyright(c)2019 Biomedical Fuzzy Systems Association"}]},"item_3_select_195":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"publisher"}]},"item_3_source_id_187":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1345-1537","subitem_source_identifier_type":"PISSN"}]},"item_3_source_id_189":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA1145146X","subitem_source_identifier_type":"NCID"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"前田, 康成","creatorNameLang":"ja"}],"nameIdentifiers":[{},{}]},{"creatorNames":[{"creatorName":"MAEDA, Yasunari","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2021-01-21"}],"displaytype":"detail","filename":"バイオメディカル・ファジィ・システム学会誌, 21(1), p49-60.pdf","filesize":[{"value":"757.0 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"バイオメディカル・ファジィ・システム学会誌, 21(1), p49-60","url":"https://kitami-it.repo.nii.ac.jp/record/8957/files/バイオメディカル・ファジィ・システム学会誌, 21(1), p49-60.pdf"},"version_id":"f24a11af-95b9-46f7-996b-2f98c51802f7"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ヘルスケア支援方法","subitem_subject_scheme":"Other"},{"subitem_subject":"マルコフ決定過程","subitem_subject_scheme":"Other"},{"subitem_subject":"EMアルゴリズム","subitem_subject_scheme":"Other"},{"subitem_subject":"半教師付き学習","subitem_subject_scheme":"Other"},{"subitem_subject":"healthcare support method","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Markov decision processes","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"EM algorithm","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"semi-supervised learning","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"マルコフ決定過程を用いたヘルスケア支援方法における半教師付き学習","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"マルコフ決定過程を用いたヘルスケア支援方法における半教師付き学習","subitem_title_language":"ja"},{"subitem_title":"Semi-supervised Learning for a Healthcare Support Method using Markov Decision Processes","subitem_title_language":"en"}]},"item_type_id":"3","owner":"1","path":["86"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2021-01-21"},"publish_date":"2021-01-21","publish_status":"0","recid":"8957","relation_version_is_last":true,"title":["マルコフ決定過程を用いたヘルスケア支援方法における半教師付き学習"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2022-12-13T02:23:29.913466+00:00"}