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    题名: A Resistant Learning Procedure for Coping with Outliers
    作者: 鄭宗記;蔡瑞煌
    Tsaih,Rua-Huan;Cheng,Tsung-Chi
    贡献者: 統計系
    关键词: Resistant learning;Outliers;Single-hidden layer feed-forward neural networks;Smallest trimmed sum of squared residuals principle;Deletion diagnostics
    日期: 2010.05
    上传时间: 2014-11-06 18:22:06 (UTC+8)
    摘要: In the context of resistant learning, outliers are the observations far away from the fitting function that is deduced from a subset of the given observations and whose form is adaptable during the process. This study presents a resistant learning procedure for coping with outliers via single-hidden layer feed-forward neural network (SLFN). The smallest trimmed sum of squared residuals principle is adopted as the guidance of the proposed procedure, and key mechanisms are: an analysis mechanism that excludes any potential outliers at early stages of the process, a modeling mechanism that deduces enough hidden nodes for fitting the reference observations, an estimating mechanism that tunes the associated weights of SLFN, and a deletion diagnostics mechanism that checks to see if the resulted SLFN is stable. The lake data set is used to demonstrate the resistant-learning performance of the proposed procedure.
    關聯: Annals of Mathematics and Artificial Intelligence57(2),161-180
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1007/s10472-010-9183-0
    DOI: 10.1007/s10472-010-9183-0
    显示于类别:[統計學系] 期刊論文

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