{"created":"2023-07-27T06:26:14.316045+00:00","id":9872,"links":{},"metadata":{"_buckets":{"deposit":"af2689b3-0d40-4585-814e-42c216a41b34"},"_deposit":{"created_by":3,"id":"9872","owners":[3],"pid":{"revision_id":0,"type":"depid","value":"9872"},"status":"published"},"_oai":{"id":"oai:kanazawa-u.repo.nii.ac.jp:00009872","sets":["934:935:936"]},"author_link":["353","14491","377"],"item_8_biblio_info_8":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2004-11-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicPageEnd":"6","bibliographicPageStart":"1","bibliographicVolumeNumber":"A5","bibliographic_titles":[{"bibliographic_title":"第19回信号処理シンポジウム，八ヶ岳"}]}]},"item_8_description_21":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"アミノ酸配列からタンパク質の二次構造を予測する階層形ニュートラルネットワークにおいて、汎化能力の向上について検討を行った。学習データにも依存するが、過学習が起こりやすい。しかし、学習に要する時間が膨大であり、学習データを増やすことなく汎化能力を高めることが望ましい。学習データが限られているので、学習データの周りで領域を広くカバーする必要がある。これは、学習中に活性化関数の傾斜を緩やかに制御することにより可能である。そのため、学習係数の制御、学習データに小さな雑音を混入、学習中における重みの減衰制御等について検討を行った。その結果、これらの方法は全て汎化能力を高めることができた。中でも、重みの抑制制御がもっとも高い予測精度を実現した。","subitem_description_type":"Abstract"}]},"item_8_description_5":{"attribute_name":"提供者所属","attribute_value_mlt":[{"subitem_description":"金沢大学理工研究域　電子情報学系","subitem_description_type":"Other"}]},"item_8_publisher_17":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"電子情報通信学会 / 基礎境界ソサイエティ / 信号処理研究専門委員会"}]},"item_8_relation_28":{"attribute_name":"関連URI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://www.ieice.org/~sip/","subitem_relation_type_select":"URI"}},{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://www.ieice.org/jpn/index.html","subitem_relation_type_select":"URI"}}]},"item_8_version_type_25":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"福村, 健一"},{"creatorName":"ナカヤマ, ケンジ","creatorNameLang":"ja-Kana"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中山, 謙二"}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"平野, 晃宏"}],"nameIdentifiers":[{},{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-10-03"}],"displaytype":"detail","filename":"TE-PR-NAKAYAMA-K-1.pdf","filesize":[{"value":"592.2 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"TE-PR-NAKAYAMA-K-1.pdf","url":"https://kanazawa-u.repo.nii.ac.jp/record/9872/files/TE-PR-NAKAYAMA-K-1.pdf"},"version_id":"33ccdc9b-c7b9-4a20-b668-f148001f892c"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"conference paper","resourceuri":"http://purl.org/coar/resource_type/c_5794"}]},"item_title":"タンパク質の２次構造予測を行うニューラルネットワークにおける汎化能力の向上","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"タンパク質の２次構造予測を行うニューラルネットワークにおける汎化能力の向上"},{"subitem_title":"Comparison of generalization methods for multilayer neural network applied to predicting protein secondary structure","subitem_title_language":"en"}]},"item_type_id":"8","owner":"3","path":["936"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-10-03"},"publish_date":"2017-10-03","publish_status":"0","recid":"9872","relation_version_is_last":true,"title":["タンパク質の２次構造予測を行うニューラルネットワークにおける汎化能力の向上"],"weko_creator_id":"3","weko_shared_id":-1},"updated":"2023-07-28T01:47:37.972645+00:00"}