政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/75846

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113318/144297 (79%)
Visitors : 51021992 Online Users : 880

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 > Item 140.119/75846

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/75846

Title:	Resistant learning on the envelope bulk for identifying anomalous patterns
Authors:	Huang, S.-Y.;Yu, Fang;Tsaih, Ray;Huang, Y. 郁方;蔡瑞煌
Contributors:	資管系
Date:	2014-09
Issue Date:	2015-06-16 15:55:15 (UTC+8)
Abstract:	Anomalous patterns are observations that lie far away from the fitting function deduced from the bulk of the given observations. This work addresses the research issue to effectively identify anomalous patterns in both contexts of resistant learning, where there is no assumption about the fitting function form, and of changing environments. The resistant learning means that the learning procedure is not impacted significantly by the outlying observations. In literature, there is the resistant learning with searching a near-perfect fitting function for identifying the bulk of the majority of observations. However, the learning algorithm with searching a near-perfect fitting function suffers from time inefficiency. To effectively identify anomalous patterns in both contexts of resistant learning and changing environments, this study proposes a new resistant learning algorithm with envelope module that learns to evolve a nonlinear fitting function wrapped with a constant-width envelope for containing the majority of observations and thus identifying anomalous patterns. An illustrative experiment is set up to justify the effectiveness of the envelope module and the experimental result shows the positive promise.
Relation:	Proceedings of the International Joint Conference on Neural Networks, 3 September 2014, 論文編號 6889485, Pages 3303-3310, 2014 International Joint Conference on Neural Networks, IJCNN 2014; Beijing; China; 6 July 2014 到 11 July 2014; 類別編號CFP14IJS-ART; 代碼 108721
Data Type:	conference
DOI 連結:	http://dx.doi.org/10.1109/IJCNN.2014.6889485
DOI:	10.1109/IJCNN.2014.6889485
Appears in Collections:	[資訊管理學系] 會議論文

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML2	1084	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...