Gaussian Logic for Predictive Classification
Abstract
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.
Cite
Text
Kuzelka et al. "Gaussian Logic for Predictive Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011. doi:10.1007/978-3-642-23783-6_18Markdown
[Kuzelka et al. "Gaussian Logic for Predictive Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011.](https://mlanthology.org/ecmlpkdd/2011/kuzelka2011ecmlpkdd-gaussian/) doi:10.1007/978-3-642-23783-6_18BibTeX
@inproceedings{kuzelka2011ecmlpkdd-gaussian,
title = {{Gaussian Logic for Predictive Classification}},
author = {Kuzelka, Ondrej and Szabóová, Andrea and Holec, Matej and Zelezný, Filip},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2011},
pages = {277-292},
doi = {10.1007/978-3-642-23783-6_18},
url = {https://mlanthology.org/ecmlpkdd/2011/kuzelka2011ecmlpkdd-gaussian/}
}