{"id":188607,"updated":"2025-01-20T01:58:50.033983+00:00","links":{},"created":"2025-01-19T00:54:44.354834+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00188607","sets":["6504:9465:9481"]},"path":["9481"],"owner":"6748","recid":"188607","title":["時系列マルチモーダル情報の分節・分類に基づくロボットによる概念の学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-03-13"},"_buckets":{"deposit":"331d9f15-7881-4030-b1f4-dec1ae9c3b9e"},"_deposit":{"id":"188607","pid":{"type":"depid","value":"188607","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"時系列マルチモーダル情報の分節・分類に基づくロボットによる概念の学習","author_link":["428007","428009","428005","428008","428006"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"時系列マルチモーダル情報の分節・分類に基づくロボットによる概念の学習"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2018-03-13","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":"電通大"},{"subitem_text_value":"電通大"},{"subitem_text_value":"電通大"},{"subitem_text_value":"電通大"},{"subitem_text_value":"電通大"}]},"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/188607/files/IPSJ-Z80-5N-05.pdf","label":"IPSJ-Z80-5N-05.pdf"},"date":[{"dateType":"Available","dateValue":"2018-05-07"}],"format":"application/pdf","filename":"IPSJ-Z80-5N-05.pdf","filesize":[{"value":"822.8 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"a7801489-f32f-4f8d-82b7-5efd61f55cca","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"布川, 遼太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"宮澤, 和貴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 友昭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"長井, 隆行"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"金子, 正秀"}],"nameIdentifiers":[{}]}]},"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":"本研究では概念を,ロボットが取得可能なセンサ情報をクラスタリングすることで形成されるカテゴリであると定義する.ロボットが概念を持つことで,一部の可観測情報から未観測情報の予測が可能となり,様々な事物に対して柔軟に対応することが可能となる.本稿では,マルチモーダルな時系列センサ情報を分節・分類することで,概念を形成可能な手法を提案する.提案手法では,隠れセミマルコフモデルを用いセンサ情報を分節・分類することで,1つの概念によって表現されるセンサ情報の時間的な範囲と,そのカテゴリを教師無しで推定する.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"186","bibliographic_titles":[{"bibliographic_title":"第80回全国大会講演論文集"}],"bibliographicPageStart":"185","bibliographicIssueDates":{"bibliographicIssueDate":"2018-03-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2018"}]},"relation_version_is_last":true,"weko_creator_id":"6748"}}