{"id":123127,"updated":"2025-01-21T03:04:57.934992+00:00","links":{},"created":"2025-01-19T00:03:14.957194+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00123127","sets":["6504:8032:8036"]},"path":["8036"],"owner":"1","recid":"123127","title":["帰納学習ツールISLによるケース分析(2) : 実現"],"pubdate":{"attribute_name":"公開日","attribute_value":"1993-03-01"},"_buckets":{"deposit":"9451e7d0-91c7-4802-9a58-af57caf9dfae"},"_deposit":{"id":"123127","pid":{"type":"depid","value":"123127","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"帰納学習ツールISLによるケース分析(2) : 実現","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"帰納学習ツールISLによるケース分析(2) : 実現"},{"subitem_title":"An application of inductive Jearning tool,ISL,to knowledge analysis and synthesis","subitem_title_language":"en"}]},"item_type_id":"22","publish_date":"1993-03-01","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":"東京理科大学 理工学部"},{"subitem_text_value":"NTTデータ通信(株)"},{"subitem_text_value":"東京理科大学 理工学部"},{"subitem_text_value":"東京理科大学 理工学部"}]},"item_22_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Science University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"NTT DATA Comm.Sys.Corp.","subitem_text_language":"en"},{"subitem_text_value":"Science University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Science University of Tokyo","subitem_text_language":"en"}]},"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/123127/files/KJ00001336953.pdf"},"date":[{"dateType":"Available","dateValue":"1993-03-01"}],"format":"application/pdf","filename":"KJ00001336953.pdf","filesize":[{"value":"171.9 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"ae8b040d-c7be-4923-86ee-e46a7f0d29c0","displaytype":"detail","licensetype":"license_note"}]},"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":"問題解決を行なう時に,人はまずその問題を分析しようとする.これは,問題となる対象から多くのデータを集め,それを細かく分類することによってその傾向を把握し,対象の全体像や特徴を明らかにすることであり,ケース分析という.一方,知識獲得のボトルネックを解消する方法として活発に研究されている帰納学習は,与えられた事例データから何らかの規則を導き出すものである.これは見方をかえると,事例データをいくつかに分類するものであるといえる.従来の帰納学習システムFOIL,GOLEMなどは基本的に記号データしか扱えないため,問題によっては,数値データを扱う必要のあるケース分析では,数値データを記号データに置き換えるという作業が必要となり,そのプロセスが複雑になってしまう.そこで我々は数値,記号の両データから同様に学習が行なえる帰納学習ツールISL(Induction System for Learning)を開発した.本稿では,この学習ツールISLをケース分析に用いた場合のその有効性と実現方法について述べる.ここでは,ケース分析の対象としてネットワークに継れた計算機環境の資源の最適利用問題を取りあげた.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"30","bibliographic_titles":[{"bibliographic_title":"全国大会講演論文集"}],"bibliographicPageStart":"29","bibliographicIssueDates":{"bibliographicIssueDate":"1993-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"人工知能及び認知科学","bibliographicVolumeNumber":"第46回"}]},"relation_version_is_last":true,"weko_creator_id":"1"}}