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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/17956

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/17956

Title:	A Stochastic Approximation View of Boosting
Authors:	張源俊 Tsao,C. Andy;Chang,Yuan-chin Ivan
Date:	2005
Issue Date:	2008-12-19 09:07:23 (UTC+8)
Abstract:	The boosting as a stochastic approximation algorithm is considered. This new interpretation provides an alternative theoretical framework for investigation. Following the results of stochastic approximation theory a stochastic approximation boosting algorithm, SABoost, is proposed. By adjusting its step sizes, SABoost will have different kinds of properties. Empirically, it is found that SABoost with a small step size will have smaller training and testing errors difference, and when the step size becomes large, it tends to overfit (i.e. bias towards training scenarios). This choice of step size can be viewed as a smooth (early) stopping rule. The performance of AdaBoost is compared and contrasted.
Relation:	Computational Statistics and Data Analysis,52(1),325-334
Data Type:	article
DOI 連結:	http://dx.doi.org/http://dx.doi.org/10.1016/j.csda.2007.06.020
DOI:	10.1016/j.csda.2007.06.020
Appears in Collections:	[統計學系] 期刊論文

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