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• W2000334459 abstract "We consider learning probabilistic graphical models in a problem domain of unknown dependence structure. Common learning algorithms rely on single-link lookahead search, which assumes the underlying domain is not pseudo-independent. Since the dependence structure of the domain is unknown, such assumption is fallible. We study learning algorithms that make no such assumption and return an approximate dependence structure no matter whether the domain is pseudo-independent or not. The focus of this paper is on learning decomposable Markov networks, which can directly be used for model-based inference or as the intermediate step for further learning of directed graphical models. We identify a small subset of domain variables, termed crux, in the graphical models currently being examined during search. We prove that crux is sufficient for computing the incremental change of both model description length as well as data description length given the model. Based on crux, we propose algorithms that reduce evaluation of alternative graphical models to local computation, improve efficiency significantly, and introduce no error to the selection of alternative models." @default.
• W2000334459 created "2016-06-24" @default.
• W2000334459 creator A5032903511 @default.
• W2000334459 creator A5079775806 @default.
• W2000334459 date "2006-05-17" @default.
• W2000334459 modified "2023-09-25" @default.
• W2000334459 title "Learning decomposable markov networks in pseudo-independent domains with local evaluation" @default.
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• W2000334459 doi "https://doi.org/10.1007/s10994-006-8366-8" @default.
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