期刊名称：Ovidius University Annals: Economic Sciences Series
电子版ISSN：2393-3127
出版年度：2011
卷号：11
期号：2
语种：English
出版社：Ovidius University Press
摘要：In this paper,we consider a number of algorithms for estimating the parameters of ME models,including iterative scaling,gradient ascent,conjugate gradient,and variable metric methods. Surprisingly,the standardly used iterative scaling algorithms perform quite poorly in comparison to the others,and for all of the test problems,a limitedmemory variable metric algorithm outperformed the other choices. Maximum entropy (ME) models,variously known as log-linear,Gibbs,exponential,and multinomial logit models,provide a general purpose machine learning technique for classification and prediction which has been successfully applied to fields as diverse as computer vision and econometrics.
