{"id":207610,"updated":"2025-01-19T19:07:06.528515+00:00","links":{},"created":"2025-01-19T01:09:14.169174+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00207610","sets":["6164:6165:7006:10401"]},"path":["10401"],"owner":"44499","recid":"207610","title":["高次元データ多クラス識別問題におけるGBDTライブラリの実装と改善"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-11-04"},"_buckets":{"deposit":"ef67f159-5b76-4499-aea8-4f3e3b38761c"},"_deposit":{"id":"207610","pid":{"type":"depid","value":"207610","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"高次元データ多クラス識別問題におけるGBDTライブラリの実装と改善","author_link":["518652","518651","518653","518650"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"高次元データ多クラス識別問題におけるGBDTライブラリの実装と改善"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"GBDT, 機械学習, 次元削減, 正則化","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2020-11-04","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"},{"subitem_text_value":"NTT ソフトウェアイノベーションセンタ"},{"subitem_text_value":"NTT ソフトウェアイノベーションセンタ"},{"subitem_text_value":"NTT ソフトウェアイノベーションセンタ"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"NTT Software Innovation Center","subitem_text_language":"en"},{"subitem_text_value":"NTT Software Innovation Center","subitem_text_language":"en"},{"subitem_text_value":"NTT Software Innovation Center","subitem_text_language":"en"},{"subitem_text_value":"NTT Software Innovation Center","subitem_text_language":"en"}]},"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/207610/files/IPSJ-DPSWS2020008.pdf","label":"IPSJ-DPSWS2020008.pdf"},"date":[{"dateType":"Available","dateValue":"2022-11-04"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DPSWS2020008.pdf","filesize":[{"value":"1.3 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":"b94611f3-fbd0-4521-ab0b-f21738ae60a4","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 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":"千々和, 大輝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"税所, 修"}],"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":"機械学習技術の普及により，高度な学習処理を伴うデータの分類や回帰分析が様々な産業・サービスにおいて用いられている．機械学習の学習処理は計算量が大きい傾向にあり，クラウドコンピューティングを用い潤沢な計算資源の元実施されるのが一般的である．また，機械学習を用いたシステムを開発する際は入出力データやハイパーパラメータなどの試行錯誤が必要になり，繰り返し学習処理を行うことが多い．そのため，機械学習システムの開発コストの削減のため，学習処理自体の計算量を削減，効率化することが望ましい．本研究では勾配ブースティング法に着目し，高次元の入力データを分類する問題において学習処理の効率化手法を検討，実装し評価を行う．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"57","bibliographic_titles":[{"bibliographic_title":"第28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":"50","bibliographicIssueDates":{"bibliographicIssueDate":"2020-11-04","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}