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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Gaussian Logic for Predictive Classification
verfasst von : Ondřej Kuželka, Andrea Szabóová, Matěj Holec, Filip Železný
Erschienen in: Machine Learning and Knowledge Discovery in Databases
Verlag: Springer Berlin Heidelberg
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We describe a statistical relational learning framework called Gaussian Logic capable to work efficiently with combinations of relational and numerical data. The framework assumes that, for a fixed relational structure, the numerical data can be modelled by a multivariate normal distribution. We demonstrate how the Gaussian Logic framework can be applied to predictive classification problems. In experiments, we first show an application of the framework for the prediction of DNA-binding propensity of proteins. Next, we show how the Gaussian Logic framework can be used to find motifs describing highly correlated gene groups in gene-expression data which are then used in a set-level-based classification method.