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CPD tree learning using contexts as background knowledge. (English) Zbl 1465.68229

Destercke, Sébastien (ed.) et al., Symbolic and quantitative approaches to reasoning with uncertainty. 13th European conference, ECSQARU 2015, Compiègne, France, July 15–17, 2015. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 9161, 356-365 (2015).
Summary: Context specific independence (CSI) is an efficient means to capture independencies that hold only in certain contexts. Inference algorithms based on CSI are capable to learn the Conditional Probability Distribution (CPD) tree relative to a target variable. We model motifs as specific contexts that are recurrently observed in data. These motifs can thus constitute a domain knowledge which can be incorporated into a learning procedure. We show that the integration of this prior knowledge provides better learning performances and facilitates the interpretation of local structure.
For the entire collection see [Zbl 1316.68008].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
62H22 Probabilistic graphical models
68T30 Knowledge representation

Software:

Tabu search
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Full Text: DOI HAL

References:

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