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Algorithms for Maximum Entropy Parameter Estimation

Author

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  • Nidelea Marinela

    („Titu Maiorescu” University of Bucharest)

Abstract

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.

Suggested Citation

  • Nidelea Marinela, 2011. "Algorithms for Maximum Entropy Parameter Estimation," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 934-937, May.
    • Handle: RePEc:ovi:oviste:v:xi:y:2011:i:9:p:934-937
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    More about this item

    Keywords

    GIS; entropy; ME models; probability; heuristic;
    All these keywords.

    JEL classification:

    • L63 - Industrial Organization - - Industry Studies: Manufacturing - - - Microelectronics; Computers; Communications Equipment

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