{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218747","sets":["1164:4402:10858:10955"]},"path":["10955"],"owner":"44499","recid":"218747","title":["縮小型最尤自己組織化マップを用いた混合ガウス分布モデルの学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-01"},"_buckets":{"deposit":"a01355e1-3b63-4966-a67f-711cf3082946"},"_deposit":{"id":"218747","pid":{"type":"depid","value":"218747","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"縮小型最尤自己組織化マップを用いた混合ガウス分布モデルの学習","author_link":["569549","569548"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"縮小型最尤自己組織化マップを用いた混合ガウス分布モデルの学習"},{"subitem_title":"Learning of Gaussian Mixture model using the Shirinking Maximum Likelihood Self-Organizing Map","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"学習","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-07-01","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"群馬大学理工学府"},{"subitem_text_value":"群馬大学情報学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Science and Technology, Gunma University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Informatics, Gunma University","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/218747/files/IPSJ-ICS22207002.pdf","label":"IPSJ-ICS22207002.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-01"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ICS22207002.pdf","filesize":[{"value":"1.9 MB"}],"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":"be252205-7c9e-4654-a8ac-d7905f43913f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"茂木, 亮祐"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"関, 庸一"}],"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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-885X","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"混合ガウス分布モデル（Gaussian Mixture Model，GMM）は有限個の多変量正規分布を混合した確率モデルであり，画像認識や音声認識，異常検知，密度推定，クラス分類におけるサブクラスの推定など教師あり・教師なし学習問わず多くの問題で有用なモデルである．GMM の学習における最も困難な課題の一つが混合数の決定であり，様々なアプローチが提案されている．本研究では，クラスタ数推定法の一つである縮小型最尤自己組織化マップ（Shrinking maximum likelihood self-organizing map，SMLSOM）を GMM の学習問題に適用し，数値実験を通じてその有用性を示す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"A Gaussian Mixture Model (GMM) is a probabilistic model of a mixture of finite multivariate normal distributions and is helpful for many problems in image recognition, speech recognition, anomaly detection, density estimation, subclass estimation classification, and other unsupervised and supervised learning tasks. One of the most challenging tasks in learning GMMs is determining the number of mixture components, and various approaches have been proposed. In this work, we apply the shrinking maximum likelihood self-organizing map (SMLSOM), one of the methods for estimating the number of clusters, to the GMM learning task and demonstrate its effectiveness through numerical experiments.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告知能システム（ICS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-07-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2022-ICS-207"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":218747,"updated":"2025-01-19T15:02:59.143616+00:00","links":{},"created":"2025-01-19T01:19:05.720861+00:00"}