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    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: http://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.
    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

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