M. Goadrich & J. Shavlik (2007).
Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates. Proceedings of the Seventeenth Conference on Inductive Logic Programming, Corvallis, Oregon.
This publication is available in PDF.
Statistical Relational Learning (SRL) combines the benefits of probabilistic machine learning approaches with complex, structured domains from Inductive Logic Programming (ILP). We propose a new SRL algorithm, GleanerSRL, to generate the probability that an example is positive within highly-skewed relational domains. In this work, we combine clauses from Gleaner, an ILP algorithm for learning a wide variety of first-order clauses, with the propositional learning technique of support vector machines to learn well-calibrated probabilities. We find that our results are comparable to SRL algorithms SAYU and SAYU-VISTA on a well-known relational testbed.
Computer Sciences Department
College of Letters and Science
University of Wisconsin - Madison
INFORMATION ~ PEOPLE ~ GRADS ~ UNDERGRADS ~ RESEARCH ~ RESOURCES
5355a Computer Sciences and Statistics ~ 1210 West Dayton Street, Madison, WI 53706
firstname.lastname@example.org ~ voice: 608-262-1204 ~ fax: 608-262-9777