Lorenza Saitta

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One of the major limitations of relational learning is due to the complexity of verifying hypotheses on examples. In this paper we investigate this task in light of recent published results, which show that many hard problems exhibit a narrow “phase transition” with respect to some order parameter, coupled with a large increase in computational complexity.(More)
In this paper an extensive experimental eval uation of an evolutionary approach to con cept learning is presented The experimen tation performed with the system G NET investigates the e ectiveness of the approach along the following dimensions Robustness with respect to parameter setting e ective ness of the MDL criterion coupled with a stochastic search(More)
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 we define and characterize the process of developing a “real-world” Machine Learning application, with its difficulties and relevant issues, distinguishing it from the popular practice of exploiting ready-to-use data sets. To this aim, we analyze and summarize the lessons learned from applying Machine Learning techniques to a variety of(More)
So far, the task of learning relations has been concerned with the acquisition of intensional descriptions of unrelated concepts. However, in many real domains concepts are strictly related to each other and the instances of one of them cannot possibly be recognized without previous recognition of other objects as instances of related concepts. A typical(More)