Mixtures of Kikuchi approximations

Mixtures of Kikuchi approximations” by Roberto Santana, Pedro Larrañaga, and J. A. Lozano. In Proceedings of the 17th European Conference on Machine Learning: ECML 2006, (Johannes Fürnkranz, Tobias Scheffer, and Myra Spiliopoulou, eds.), 2006, pp. 365-376.


Mixtures of distributions concern modeling a probability distribution by a weighted sum of other distributions. Kikuchi approximations of probability distributions follow an approach to approximate the free energy of statistical systems. In this paper, we introduce the mixture of Kikuchi approximations as a probability model. We present an algorithm for learning Kikuchi approximations from data based on the expectation-maximization (EM) paradigm. The proposal is tested in the approximation of probability distributions that arise in evolutionary computation.

BibTeX entry:

   author = {Roberto Santana and Pedro Larra{\~n}aga and J. A. Lozano},
   editor = {Johannes F{\"u}rnkranz and Tobias Scheffer and Myra Spiliopoulou},
   title = {Mixtures of {K}ikuchi approximations},
   booktitle = {Proceedings of the 17th European Conference on Machine
	Learning: ECML 2006},
   series = {Lecture Notes in Artificial Intelligence},
   volume = {4212},
   pages = {365-376},
   publisher = {Springer},
   year = {2006},
   url = {http://dx.doi.org/10.1007/11871842_36}

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