@inproceedings{
 title = {Mixtures of Kikuchi approximations},
 type = {inproceedings},
 year = {2006},
 keywords = {isg_ehu},
 pages = {365-376},
 volume = {4212},
 publisher = {Springer},
 series = {Lecture Notes in Artificial Intelligence},
 id = {ef57231d-9106-3f83-9fc5-568c8497557c},
 created = {2021-11-12T08:30:31.739Z},
 file_attached = {false},
 profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},
 group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},
 last_modified = {2021-11-12T08:30:31.739Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {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.},
 bibtype = {inproceedings},
 author = {Santana, R and Larrañaga, Pedro and Lozano, J A},
 editor = {Fürnkranz, Johannes and Scheffer, Tobias and Spiliopoulou, Myra},
 booktitle = {Proceedings of the 17th European Conference on Machine Learning: ECML 2006}
}

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