To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilist...
Hanna Pasula, Luke S. Zettlemoyer, Leslie Pack Kae...
In this paper we first overview the main concepts of Statistical Learning Theory, a framework in which learning from examples can be studied in a principled way. We then briefly di...
Abstract. We outline a general theory of graph polynomials which covers all the examples we found in the vast literature, in particular, the chromatic polynomial, various generaliz...
Given the proven usefulness of ontologies in many areas, the representation of logical axioms associated to ontological concepts and relations has become an important task in order...
Luis Del Vasto Terrientes, Antonio Moreno, David S...
Multi-label classification is a popular learning task. However, some of the algorithms that learn from multi-label data, can only output a score for each label, so they cannot be r...
Marios Ioannou, George Sakkas, Grigorios Tsoumakas...