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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)
Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning and of sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon and the extensive experimental(More)