{"_buckets": {"deposit": "9f2db0b1-2e12-466c-81a0-b4e81e51cd39"}, "_deposit": {"id": "8955", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "8955"}, "status": "published"}, "_oai": {"id": "oai:kitami-it.repo.nii.ac.jp:00008955", "sets": ["86"]}, "author_link": ["273", "90500", "7687", "90501"], "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": "A Note on Telegram Categorization Algorithm Based upon Statistical Decision Theory", "subitem_alternative_title_language": "en"}]}, "item_3_biblio_info_186": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2002-10-15", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "10", "bibliographicPageEnd": "3126", "bibliographicPageStart": "3119", "bibliographicVolumeNumber": "43", "bibliographic_titles": [{"bibliographic_title": "情報処理学会論文誌"}, {"bibliographic_title": "Transactions of Information Processing Society of Japan", "bibliographic_titleLang": "en"}]}]}, "item_3_description_184": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "従来，電報の分類作業は人手で行われており，その自動化が望まれている．電報分類問題については文書分類問題と同様の定式化を行うことができる．しかし，従来の文書分類方法には，学習用データが有限の場合にはその分類精度に何ら理論的な保証がない．  そこで，本研究では統計的決定理論に基づいて，学習用データが有限の場合に電報を間違った分野に分類してしまう確率である誤り率をベイズ基準のもとで最小にすることが保証された電報分類方法を提案する．さらに，実際の分類実験を通して情報検索や文書分類で利用されているベクトル空間法を電報分類に適用した場合の基本的なアルゴリズムと提案方法との比較を行い，提案方法の方がベクトル空間法を電報分類に適用した場合のアルゴリズムよりも分類精度が高いことを示す．また，同じく文書分類等で利用されているNaive-Bayes法を電報分類に適用した場合のアルゴリズムと提案方法との比較を，そのアルゴリズムの導出過程の違いから考察する．", "subitem_description_type": "Abstract"}, {"subitem_description": "[ENG]\nUsually telegram categorization is done by human.And an automatic method for telegram categorization is needed.Telegram categorization problem can be resolved by using the same mathematical model of text categorization.But algorithms in previous research on text categorization have no theoretical guarantee.So in this research we propose a new algorithm for telegram categorization,which is based upon statistical decision theory and minimizes an error rate with respect to Bayes criterion.And we show an effectiveness of our proposed algorithm by some simulations.", "subitem_description_type": "Abstract"}]}, "item_3_publisher_212": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "情報処理学会"}]}, "item_3_relation_208": {"attribute_name": "論文ID（NAID）", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "110002771166", "subitem_relation_type_select": "NAID"}}]}, "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": "1882-7764", "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": "AN00116647", "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": [{"nameIdentifier": "273", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "30422033", "nameIdentifierScheme": "KAKEN - 研究者検索", "nameIdentifierURI": "https://nrid.nii.ac.jp/ja/nrid/1000030422033/"}]}, {"creatorNames": [{"creatorName": "小原, 永", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "90500", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "MAEDA, Yasunari", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "7687", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "OHARA, HISASHI", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "90501", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2021-01-20"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "情報処理学会論文誌, 43(10), pp.3119-3126.pdf", "filesize": [{"value": "184.5 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 184500.0, "url": {"label": "情報処理学会論文誌, 43(10), pp.3119-3126", "url": "https://kitami-it.repo.nii.ac.jp/record/8955/files/情報処理学会論文誌, 43(10), pp.3119-3126.pdf"}, "version_id": "bc3e6ebe-8622-429b-8191-34389994bfd9"}]}, "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": "統計的決定理論に基づく電報分類方法に関する一考察", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "統計的決定理論に基づく電報分類方法に関する一考察", "subitem_title_language": "ja"}, {"subitem_title": "A Note on Telegram Categorization Algorithm Based upon Statistical Decision Theory", "subitem_title_language": "en"}]}, "item_type_id": "3", "owner": "1", "path": ["86"], "permalink_uri": "https://kitami-it.repo.nii.ac.jp/records/8955", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2021-01-20"}, "publish_date": "2021-01-20", "publish_status": "0", "recid": "8955", "relation": {}, "relation_version_is_last": true, "title": ["統計的決定理論に基づく電報分類方法に関する一考察"], "weko_shared_id": -1}