{"created":"2021-03-01T07:07:55.158340+00:00","id":29430,"links":{},"metadata":{"_buckets":{"deposit":"69b7b2d9-049c-4ef0-b7fd-4d6765d02445"},"_deposit":{"id":"29430","owners":[],"pid":{"revision_id":0,"type":"depid","value":"29430"},"status":"published"},"_oai":{"id":"oai:tsukuba.repo.nii.ac.jp:00029430","sets":["152:2529","3:62:5596:2564"]},"item_5_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2013-10","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"275","bibliographicPageStart":"260","bibliographicVolumeNumber":"46","bibliographic_titles":[{"bibliographic_title":"Neural networks"}]}]},"item_5_creator_3":{"attribute_name":"著者別名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"日野, 英逸"}],"nameIdentifiers":[{}]}]},"item_5_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"The Shannon information content is a valuable numerical characteristic of probability distributions. The problem of estimating the information content from an observed dataset is very important in the fields of statistics, information theory, and machine learning. The contribution of the present paper is in proposing information estimators, and showing some of their applications. When the given data are associated with weights, each datum contributes differently to the empirical average of statistics. The proposed estimators can deal with this kind of weighted data. Similar to other conventional methods, the proposed information estimator contains a parameter to be tuned, and is computationally expensive. To overcome these problems, the proposed estimator is further modified so that it is more computationally efficient and has no tuning parameter. The proposed methods are also extended so as to estimate the cross-entropy, entropy, and Kullback–Leibler divergence. Simple numerical experiments show that the information estimators work properly. Then, the estimators are applied to two specific problems, distribution-preserving data compression, and weight optimization for ensemble regression.","subitem_description_type":"Abstract"}]},"item_5_identifier_34":{"attribute_name":"URI","attribute_value_mlt":[{"subitem_identifier_type":"HDL","subitem_identifier_uri":"http://hdl.handle.net/2241/120041"}]},"item_5_publisher_27":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Elsevier Ltd."}]},"item_5_relation_10":{"attribute_name":"PubMed番号","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"23859828","subitem_relation_type_select":"PMID"}}]},"item_5_relation_11":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1016/j.neunet.2013.06.005","subitem_relation_type_select":"DOI"}}]},"item_5_rights_12":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© 2013 Elsevier Ltd.　　NOTICE: this is the author’s version of a work that was accepted for publication in Neural networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural networks, 46, 2013, http://dx.doi.org/10.1016/j.neunet.2013.06.005"}]},"item_5_select_15":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"author"}]},"item_5_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0893-6080","subitem_source_identifier_type":"ISSN"}]},"item_5_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11540311","subitem_source_identifier_type":"NCID"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hino, Hideitsu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Murata, Noboru"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2014-05-09"}],"displaytype":"detail","filename":"NN_46.pdf","filesize":[{"value":"1.6 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"NN_46.pdf","url":"https://tsukuba.repo.nii.ac.jp/record/29430/files/NN_46.pdf"},"version_id":"de948534-a502-41fa-b012-8c8ad93b92e1"}]},"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":"Information estimators for weighted observations","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Information estimators for weighted observations"}]},"item_type_id":"5","owner":"1","path":["2529","2564"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-11-14"},"publish_date":"2013-11-14","publish_status":"0","recid":"29430","relation_version_is_last":true,"title":["Information estimators for weighted observations"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-04-27T08:57:52.096347+00:00"}