Algorithms for Maximum Entropy Parameter Estimation
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.
Volume (Year): XI (2011)
Issue (Month): 2 (May)
|Contact details of provider:|| Web page: http://www.univ-ovidius.ro/facultatea-de-stiinte-economice|
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:ovi:oviste:v:xi:y:2011:i:9:p:934-937. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jeflea Victor)
If references are entirely missing, you can add them using this form.