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.
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