{"created":"2025-01-18T23:15:32.835862+00:00","updated":"2025-01-22T07:25:35.741456+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00050870","sets":["1164:4402:4467:4469"]},"path":["4469"],"owner":"1","recid":"50870","title":["クラスタリングに対する例からの学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"1995-09-12"},"_buckets":{"deposit":"231187e1-640e-436a-803a-910aa498ee2b"},"_deposit":{"id":"50870","pid":{"type":"depid","value":"50870","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"クラスタリングに対する例からの学習","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"クラスタリングに対する例からの学習"},{"subitem_title":"Learning from Examples for Clustering","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"1995-09-12","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"電子技術総合研究所推論研究室"},{"subitem_text_value":"電子技術総合研究所推論研究室"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Machine Inference Section, Electrotechnical Laboratory","subitem_text_language":"en"},{"subitem_text_value":"Machine Inference Section, Electrotechnical Laboratory","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/50870/files/IPSJ-ICS95101004.pdf"},"date":[{"dateType":"Available","dateValue":"1997-09-12"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ICS95101004.pdf","filesize":[{"value":"493.5 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":"25"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"ccf98801-820e-4dac-8d68-7ab659f7e7a4","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 1995 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"神嶌, 敏弘"},{"creatorName":"新田, 克己"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Toshihiro, Kamishima","creatorNameLang":"en"},{"creatorName":"Katsumi, Nitta","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11135936","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":"本研究では，分類対象の集合を，「似ている」ものどうしを集めたクラスタなる部分集合に分割するクラスタリングを対象にした「クラスタリングに対する例からの学習」を取り上げ，この学習を行った．クラスタリングは，画像処理の領域分割などの分野で利用されているが，クラスタリングの利用者が望む分割を獲得することが困難であることが多い．そこで，分類対象の集合とその集合の望ましい分割の組である学習事例の集合から，未知の分類対象の集合に対する望ましい分割を獲得するための規準を獲得する学習を新たに考えた．従来の例からの学習と数値分類の手法を組み合わせてこの学習を行い，その結果を評価した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"We solve the novel machine learning problem: \"Learning from Examples for Clustering,\" that handles the clustering, that is the method to divide the set of individuals to the subsets containing the \"similar\" individuals. Though the clustering is the method used in image understanding and some other fields, clustering users often gain the partitions which they do not wish. Therefore, we try an approach to get desirable partition for an unknown set of individuals from the set of examples, which are pairs of a set of individuals and the desirable partition for the set. We try this approach with the technique of the learning from examples and the numerical taxonomy, and propose criteria to evaluate results of this learning problem.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"24","bibliographic_titles":[{"bibliographic_title":"情報処理学会研究報告知能と複雑系（ICS）"}],"bibliographicPageStart":"19","bibliographicIssueDates":{"bibliographicIssueDate":"1995-09-12","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"86(1995-ICS-101)","bibliographicVolumeNumber":"1995"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"id":50870,"links":{}}