{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213365","sets":["6164:6165:7006:10727"]},"path":["10727"],"owner":"44499","recid":"213365","title":["高次元入力データのための誤差逆伝搬を用いたGBDT実装の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-10-18"},"_buckets":{"deposit":"5677393f-7aeb-485b-80a7-2d7fd72ff6c2"},"_deposit":{"id":"213365","pid":{"type":"depid","value":"213365","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"高次元入力データのための誤差逆伝搬を用いたGBDT実装の検討","author_link":["545834","545833"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"高次元入力データのための誤差逆伝搬を用いたGBDT実装の検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"勾配ブースティング，誤差逆伝播法，画像認識","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2021-10-18","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"NTTソフトウェアイノベーションセンター"},{"subitem_text_value":"NTTソフトウェアイノベーションセンター"}]},"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/213365/files/IPSJ-DPSWS2021012.pdf","label":"IPSJ-DPSWS2021012.pdf"},"date":[{"dateType":"Available","dateValue":"2023-10-18"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DPSWS2021012.pdf","filesize":[{"value":"1.1 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":"34"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"f0d668ca-4eb2-4208-a6fb-e42c43874c5f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"藤野, 知之"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"柏木, 啓一郎"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"勾配ブースティング法（GBDT）はさまざまな利用シーンで用いられ，特にデータベースのようなテーブルデータや IoT のセンサデータを用いた機械学習に頻繁に用いられている．一方，映像や画像・自然言語・音声といったメディアデータの分類においてはニューラルネットワークを用いた深層学習が一般的によく用いられており，GBDT はそのアルゴリズム的な構造から，メディアデータのような高次元データにおいて精度面で深層学習に劣る．本研究では，GBDT にニューラルネットワークの学習アルゴリズムである誤差逆伝播法の考え方を導入し，高次元データを高精度で扱えるよう拡張を施した．これにより，メディアデータの機械学習において，従来深層学習一択であったユーザーの選択肢を広げるとともに，GBDT の適用可能な問題範囲を広げる可能性を示した．本稿では，アルゴリズムの提案，実装そして画像データセットによる性能評価について述べる．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"91","bibliographic_titles":[{"bibliographic_title":"第29回マルチメディア通信と分散処理ワークショップ論文集"}],"bibliographicPageStart":"86","bibliographicIssueDates":{"bibliographicIssueDate":"2021-10-18","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":213365,"updated":"2025-01-19T17:11:11.401192+00:00","links":{},"created":"2025-01-19T01:14:14.881732+00:00"}