In Machine Learning, we are interested in:
We have strong competence in the area of Relational Learning (also called Inductive Logic Programming --- ILP) that focuses on learning knowledge expressed in subsets of first-order logics. In this field, most of our studies regards supervised learning (classification) with positive and negative examples. We have also addressed problems in Statistical Relational Learning. There are other topics of our interest such as mining frequent patterns in relational databases. More generally, we are interested in learning models expressed by patterns or rules, dealing with qualitative or quantitative attributes and classical or relational data.
In the context of unsupervised learning (Clustering), for the past 10 years, members of our research team have been consistently conducting research in this field. In particular, we have proposed novel model of:
Besides the theoretical issues associated with the development of these models, we are interested in the application of them to real world problems, particularly in the areas of text mining, information retrieval and bioinformatics.