Veröffentlichung

Titel:

A Generalized Iterative Scaling Algorithm for Maximum Entropy Model Computations Respecting Probabilistic Independencies

AutorInnen:

Marco Wilhelm
Gabriele Kern-Isberner
Marc Finthammer
Christoph Beierle

Kategorie:

Konferenzbandbeiträge

erschienen in:

Proceedings of the 10th International Symposium on Foundations of Information and Knowledge Systems (FoIKS-2018), Vol. 10833, pp. 379--399 (2018)

Abstract:

Maximum entropy distributions serve as favorable models for commonsense reasoning based on probabilistic conditional knowledge bases. Computing these distributions requires solving high-dimensional convex optimization problems, especially if the conditionals are composed of first-order formulas. In this paper, we propose a highly optimized variant of generalized iterative scaling for computing maximum entropy distributions. As a novel feature, our improved algorithm is able to take probabilistic independencies into account that are established by the principle of maximum entropy. This allows for exploiting the logical information given by the knowledge base, represented as weighted conditional impact systems, in a very condensed way.

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Springer

BibTeX-Eintrag:

@InProceedings{WilhelmKern-IsbernerFinthammerBeierle2018a, author = {Marco Wilhelm and Gabriele Kern{-}Isberner and Marc Finthammer and Christoph Beierle}, title = {A Generalized Iterative Scaling Algorithm for Maximum Entropy Model Computations Respecting Probabilistic Independencies}, booktitle = {Proceedings of the 10th International Symposium on Foundations of Information and Knowledge Systems ({FoIKS}-2018)}, year = {2018}, editor = {F. Ferrarotti and S. Woltran}, volume = {10833}, series = {LNCS}, pages = {379--399}, publisher = {Springer}, url = {https://doi.org/10.1007/978-3-319-90050-6\_21}, }