{"created":"2023-05-15T15:01:31.066909+00:00","id":83449,"links":{},"metadata":{"_buckets":{"deposit":"46e0ae28-a25b-482b-8d18-f8b5b252db28"},"_deposit":{"created_by":1,"id":"83449","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"83449"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00083449","sets":["1"]},"author_link":["1064355","1064352","1064353","1064351","1064354"],"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2021-08","bibliographicIssueDateType":"Issued"},"bibliographicPageStart":"139","bibliographicVolumeNumber":"7","bibliographic_titles":[{"bibliographic_title":"npj Computational Materials"}]}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"The automated stopping of a spectral measurement with active learning is proposed. The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression. It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with a sufficient accuracy and reduced data size. The proposed method is not only a proof-of- concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for high-throughput experiments in the era of materials informatics.","subitem_description_type":"Abstract"}]},"item_8_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Springer Nature"}]},"item_8_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1038/s41524-021-00606-5","subitem_relation_type_select":"DOI"}}]},"item_8_relation_17":{"attribute_name":"関連サイト","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1038/s41524-021-00606-5","subitem_relation_type_select":"DOI"}}]},"item_8_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2057-3960","subitem_source_identifier_type":"ISSN"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tetsuro, Ueno"}],"nameIdentifiers":[{"nameIdentifier":"1064351","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Hideaki, Ishibashi"}],"nameIdentifiers":[{"nameIdentifier":"1064352","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Hideitsu, Hino"}],"nameIdentifiers":[{"nameIdentifier":"1064353","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kanta, Ono"}],"nameIdentifiers":[{"nameIdentifier":"1064354","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Tetsuro, Ueno","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"1064355","nameIdentifierScheme":"WEKO"}]}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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":"Automated stopping criterion for spectral measurements with active learning","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Automated stopping criterion for spectral measurements with active learning"}]},"item_type_id":"8","owner":"1","path":["1"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-07-21"},"publish_date":"2021-07-21","publish_status":"0","recid":"83449","relation_version_is_last":true,"title":["Automated stopping criterion for spectral measurements with active learning"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T16:48:52.167937+00:00"}