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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/71192

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/71192

Title:	A Resistant Learning Procedure for Coping with Outliers
Authors:	鄭宗記;蔡瑞煌 Tsaih,Rua-Huan;Cheng,Tsung-Chi
Contributors:	統計系
Keywords:	Resistant learning;Outliers;Single-hidden layer feed-forward neural networks;Smallest trimmed sum of squared residuals principle;Deletion diagnostics
Date:	2010.05
Issue Date:	2014-11-06 18:22:06 (UTC+8)
Abstract:	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.
Relation:	Annals of Mathematics and Artificial Intelligence57(2),161-180
Data Type:	article
DOI 連結:	http://dx.doi.org/10.1007/s10472-010-9183-0
DOI:	10.1007/s10472-010-9183-0
Appears in Collections:	[統計學系] 期刊論文

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