Learning logical probabilistic languages

Probabilistic logic languages are particularly useful in machine learning problems where the data are characterized by uncertainty and complex relationships between the entities of interest. The learning algorithms proposed for these languages have proven to be particularly effective on a wide range of datasets. In this project we will develop algorithms for learning probabilistic logic languages based on logic programming. The hybrid server will be used to test the algorithms developed by the group and compare them with state of the art algorithms. We will measure the accuracy and the speed of the algorithms. The large memory capacity of the server will allow testing the algorithms on large datasets.

Principal investigator: Fabrizio Riguzzi rzf@unife.it
http://www.ing.unife.it/docenti/FabrizioRiguzzi/
Promoters: Bellodi Elena blllne2@unife.it
http://sites.unife.it/ml/people/elena-bellodi
Project Home Page: http://sites.unife.it/ml