## BibTeX Entry

@inproceedings{cernoch2011-rules,
file = {bumphist.pdf},
category = {ida-publications},
author = {Radom{\'i}r {\v C}ernoch and Filip {\v Z}elezn{\'y}},
title = {Probabilistic Rule Learning through Integer Linear Programming},
booktitle = {Sborn{\'i}k p{\v r}{\'i}sp{\v e}vk{\r u} 10. ro{\v c}n{\'i}ku konference ZNALOSTI},
editor = {Radim Jirou{\v s}ek and Vojtĕch Sv{\'a}tek},
year = {2011},
language = {English},
abstract = {Recent interest in probabilistic logic as a formalism for machine learning has motivated the formulation of “probabilistic rule learning”, where the task is to induce a set of logical rules from a probabilistic database. We start by defining rule learning within propositional, probabilistic logic. Then we show that this problem can be viewed as a regression task with integer variables. This problem is translated into Mixed Linear Programming, which is experimentally shown to provide a speed-up over the current implementation. The advantage of this approach is the ability to switch between logically interpretable rule learning with binary coefficients and classical regression with rational coefficients.},
vvvs = {1},
}