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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Bottom-Up Generative Modeling of Tree-Structured Data
verfasst von : Davide Bacciu, Alessio Micheli, Alessandro Sperduti
Erschienen in: Neural Information Processing. Theory and Algorithms
Verlag: Springer Berlin Heidelberg
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We introduce a compositional probabilistic model for tree-structured data that defines a bottom-up generative process from the leaves to the root of a tree. Contextual state transitions are introduced from the joint configuration of the children to the parent nodes, allowing hidden states to model the co-occurrence of substructures among the child subtrees. A mixed memory approximation is proposed to factorize the joint transition matrix as a mixture of pairwise transitions. A comparative experimental analysis shows that the proposed approach is able to better model deep structures with respect to top-down approaches.