IDA - Intelligent Data Analysis Research Group

BibTeX Entry

  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},

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