WEKO3
インデックスリンク
アイテム
{"_buckets": {"deposit": "2fc67c87-3a8f-4555-b9f3-bc2253a719f9"}, "_deposit": {"created_by": 3, "id": "7797", "owners": [3], "pid": {"revision_id": 0, "type": "depid", "value": "7797"}, "status": "published"}, "_oai": {"id": "oai:kanazawa-u.repo.nii.ac.jp:00007797", "sets": ["936"]}, "author_link": ["353", "10603"], "item_8_biblio_info_8": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "1998-05-01", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "2252", "bibliographicPageStart": "2247", "bibliographicVolumeNumber": "34", "bibliographic_titles": [{"bibliographic_title": "IEEE International Conference on Neural Networks - Conference Proceedings"}]}]}, "item_8_description_21": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "In this paper, a training data selection method for multilayer neural networks (MLNNs) in on-line training is proposed. Purpose of the reduction in training data is reducing the computation complexity of the training and saving the memory to store the data without losing generalization performance. This method uses a pairing method, which selects the nearest neighbor data by finding the nearest data in the different classes. The network is trained by the selected data. Since the selected data located along data class boundary, the trained network can guarantee generalization performance. Efficiency of this method for the on-line training is evaluated by computer simulation.", "subitem_description_type": "Abstract"}]}, "item_8_description_5": {"attribute_name": "提供者所属", "attribute_value_mlt": [{"subitem_description": "金沢大学大学院自然科学研究科情報システム", "subitem_description_type": "Other"}]}, "item_8_publisher_17": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Institute of Electrical and Electronics Engineers (IEEE)"}]}, "item_8_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1098-7576", "subitem_source_identifier_type": "ISSN"}]}, "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": "Hara, Kazuyuki"}], "nameIdentifiers": [{"nameIdentifier": "10603", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Nakayama, Kenji"}], "nameIdentifiers": [{"nameIdentifier": "353", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "00207945", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://kaken.nii.ac.jp/ja/search/?qm=00207945"}, {"nameIdentifier": "00207945", "nameIdentifierScheme": "研究者番号", "nameIdentifierURI": "https://nrid.nii.ac.jp/nrid/1000000207945"}]}]}, "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", "download_preview_message": "", "file_order": 0, "filename": "TE-PR-NAKAYAMA-K-2247.pdf", "filesize": [{"value": "294.8 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 294800.0, "url": {"label": "TE-PR-NAKAYAMA-K-2247.pdf", "url": "https://kanazawa-u.repo.nii.ac.jp/record/7797/files/TE-PR-NAKAYAMA-K-2247.pdf"}, "version_id": "7d9bd2b9-5e0f-47b2-9996-f97b59c6b15d"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "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": "A training data selection in on-line training for multilayer neural networks", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "A training data selection in on-line training for multilayer neural networks"}]}, "item_type_id": "8", "owner": "3", "path": ["936"], "permalink_uri": "http://hdl.handle.net/2297/6887", "pubdate": {"attribute_name": "公開日", "attribute_value": "2017-10-03"}, "publish_date": "2017-10-03", "publish_status": "0", "recid": "7797", "relation": {}, "relation_version_is_last": true, "title": ["A training data selection in on-line training for multilayer neural networks"], "weko_shared_id": -1}
A training data selection in on-line training for multilayer neural networks
http://hdl.handle.net/2297/6887
http://hdl.handle.net/2297/6887f9f98107-5eee-44c0-ace7-004d1d230f08
名前 / ファイル | ライセンス | アクション |
---|---|---|
TE-PR-NAKAYAMA-K-2247.pdf (294.8 kB)
|
|
Item type | 会議発表論文 / Conference Paper(1) | |||||
---|---|---|---|---|---|---|
公開日 | 2017-10-03 | |||||
タイトル | ||||||
タイトル | A training data selection in on-line training for multilayer neural networks | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
資源タイプ | conference paper | |||||
著者 |
Hara, Kazuyuki
× Hara, Kazuyuki× Nakayama, Kenji |
|||||
提供者所属 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 金沢大学大学院自然科学研究科情報システム | |||||
書誌情報 |
IEEE International Conference on Neural Networks - Conference Proceedings 巻 34, p. 2247-2252, 発行日 1998-05-01 |
|||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1098-7576 | |||||
出版者 | ||||||
出版者 | Institute of Electrical and Electronics Engineers (IEEE) | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | In this paper, a training data selection method for multilayer neural networks (MLNNs) in on-line training is proposed. Purpose of the reduction in training data is reducing the computation complexity of the training and saving the memory to store the data without losing generalization performance. This method uses a pairing method, which selects the nearest neighbor data by finding the nearest data in the different classes. The network is trained by the selected data. Since the selected data located along data class boundary, the trained network can guarantee generalization performance. Efficiency of this method for the on-line training is evaluated by computer simulation. | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |