Lorenza Saitta

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Machine learning strongly relies on the covering test to assess whether a candidate hypothesis covers training examples. The present paper investigates learning relational concepts from examples, termed relational learning or inductive logic programming. In particular, it investigates the chances of success and the computational cost of relational learning,(More)
In this paper an extensive experimental evaluation of an evolutionary approach t o c o n-cept learning is presented. The experimentation , performed with the system G-NET, investigates the eeectiveness of the approach along the following dimensions: Robustness with respect to parameter setting, eeective-ness of the MDL criterion coupled with a stochastic(More)
This paper presents the system WHY, which learns and updates a diagnostic knowledge base using domain knowledge and a set of examples. The a-priori knowledge consists of a causal model of the domain, stating the relationships among basic phenomena, and a body of phenomenological theory, describing the links between abstract concepts and their possible(More)