Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Michelangelo Ceci,Jaakko Hollmén,Sašo Džeroski Conference proce

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B,L,: Summarizing and Forecasting Time Series with Patternse of summarizing the data by compressing it using as few bits as possible, and automatically tunes all its parameters; (4) general: it applies to any domain of time series data, and can make use of multidimensional (i.e. coevolving) time series.

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Subjectively Interesting Connecting Treesstingness of such trees mathematically, taking in account any prior beliefs the user has specified about the network. We then propose heuristic algorithms to find the best trees efficiently, given a specified prior belief model. Modeling the user’s prior belief state is however not necessarily compu

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Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sourcesrequire gold-labelled data and can make predictions at any location in an area of interest given only sparse observations. We show empirically that our approach can handle noisy and biased data sources, and that simultaneously inferring reliability and transferring information between neighbouring r

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Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Co the proposed method is compared to an existing constraint-based method (RFCI) using data generated from several previously published Bayesian networks. The structural Hamming distances of the output models improved when using the proposed method compared to RFCI, especially for small sample sizes.

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