{"_buckets": {"deposit": "2b797850-e7b2-4c3b-b82b-e3b6c737ca48"}, "_deposit": {"created_by": 27, "id": "1986", "owners": [27], "pid": {"revision_id": 0, "type": "depid", "value": "1986"}, "status": "published"}, "_oai": {"id": "oai:repo.lib.tut.ac.jp:00001986", "sets": ["9"]}, "author_link": ["3794", "3678", "3722", "3795", "2102", "1945", "3796", "3724", "3336"], "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2017-04-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "4", "bibliographicPageEnd": "555", "bibliographicPageStart": "544", "bibliographicVolumeNumber": "J100-D", "bibliographic_titles": [{"bibliographic_title": "電子情報通信学会論文誌 D"}, {"bibliographic_title": "The IEICE Transactions on Electronics (Japanese Edition)", "bibliographic_titleLang": "en"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "本論文では，観測頻度から条件付き確率を推定するという問題に取り組む．条件付き確率の推定は，データマイニングや実際の応用における基本的な操作であり，その推定方法によって手法の正確さが左右されることがある．一般に，確率推定では最ゆう推定値が用いられるが，低頻度に弱いという問題がある．この問題に対処するため，ベイズの枠組みがよく用いられる．ベイズの枠組みでは，データについての事前分布を推定し，事後分布の期待値を用いる．しかし，データをもとに事前分布を推定することは容易ではない．そこで，本論文では，事前分布として何らかの分布を仮定して事後分布の信頼区間を求め，その下限値を用いる手法を提案する．期待値は偏りのない推定値となる一方で，信頼区間の下限値は条件付き確率を保守的に見積もった推定値となる．実験によって，提案手法が低頻度に頑強であることを示す．更に，提案手法は事前分布として一様分布を用いた場合，ベイズの枠組みを用いた手法とほぼ同じ性能を獲得しうることを示す．", "subitem_description_type": "Abstract"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "電子情報通信学会"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type": "isIdenticalTo", "subitem_relation_type_id": {"subitem_relation_type_id_text": "10.14923/transinfj.2016DEP0016", "subitem_relation_type_select": "DOI"}}]}, "item_10001_relation_17": {"attribute_name": "関連サイト", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "IEICE 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