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12篇 您的检索式：作者名="Hongshik"

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		题名	作者	年代	出处	被引量
	1	ltMatters Where You Go Outward Foreign Direct Investmentand Multina- tional Employment Growth at Home显示文摘	Peter Debaere Hongshik Lee Joonhyung Lee	2010	Journal of Development Economics2010，91，：	1
	2	Tree-structured logistic model for over-dispersed binomial data with application to modeling development effects显示文摘	Hongshik A James JC	1997	Biometrics1997，，53：	1
	3	In Search of Optimised Regional Trade Agreements and Applications to East Asia 显示文摘	Hongshik Lee Innwon Park	2007	The World Economy2007，，10：	1
	4	The Individual Investor and the Weekend Effect: A Reexamination with Intraday Data 显示文摘	Raymond M B K Hongshik	1997	The Quarterly Review of Economics and Finance1997，，：	1
	5	Tree-structured logistic models for over-dispersed binomial data with application to modeling development effects 显示文摘	Hongshik A James JC	1997	Biometrics1997，53，：	1
	6	Trade structure, FTAS,and Economic Growth 显示文摘	Chan-Hvun Sohn and Hongshik Lee		Review of Development Economics 20 1 0 14(3)0，，：	1
	7	Transesterification of palm oil using supercritical methanol显示文摘	Song EunSeok Lim Jungwon Lee Hongshik	2008	Supercritical Fluids2008，44，3：	1
	8	Does Korea Follow Japan in Foreign Aid? Relationships between Aid and Foreign Investment显示文摘	Sung Jing Kang Hongshik Lee Bokyeong Park 2011	2011	Japan and the World Economy2011，，23：	1
	9	Feeding Acutely Stimulates Fibrinogen Synthesis in Healthy Young and Elderly Adults1,2显示文摘	Caso Giuseppe Mileva Izolda Kelly Patricia Ahn Hongshik Gelato Marie C McNurlan Margaret A	2009	The Journal of Nutrition2009，，11：	1
	10	A decision support system to facilitate management of patients with acute gastrointestinal bleeding显示文摘	Adrienne Chu Hongshik Ahn Bhawna Halwan Bruce Kalmin Everson L.A. Artifon Alan Barkun Michail G. Lagoudakis Atul Kumar	2007	Artificial Intelligence In Medicine2007，，3：	1
	11	The individual investor and the weekend effect:A reexamination with intraday data显示文摘	Hongshik Kim	1997	The Quarterly Review of Economics and Finance Volume 37 Issue 3 Autumn 19971997，，3：	1
	12	Item Response Theory Based Ensemble in Machine Learning显示文摘In this article,we propose a novel probabilistic framework to improve the accuracy of a weighted majority voting algorithm.In order to assign higher weights to the classifiers which can correctly classify hard-to-classify instances,we introduce the item response theory(IRT)framework to evaluate the samples′difficulty and classifiers′ability simultaneously.We assigned the weights to classifiers based on their abilities.Three models are created with different assumptions suitable for different cases.When making an inference,we keep a balance between the accuracy and complexity.In our experiment,all the base models are constructed by single trees via bootstrap.To explain the models,we illustrate how the IRT ensemble model constructs the classifying boundary.We also compare their performance with other widely used methods and show that our model performs well on 19 datasets.	Ziheng Chen Hongshik Ahn	2020	International Journal of Automation and computing2020，17，5：	0

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