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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)

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… (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 an algorithm for inferring a Structured Hidden Markov Model (S-HMM) from a set of sequences. The S-HMMs are a sub-class of the Hierarchical Hidden Markov Models and are well suited to problems of process/user profiling. The learning algorithm is unsupervised, and follows a mixed bottom-up/top-down strategy, in which elementary facts in… (More)

In this paper we propose the framework of Monte Carlo algorithms as a useful one to analyze ensemble learning. In particular, this framework allows one to guess when bagging will be useful, explains why increasing the margin improves performances, and suggests a new way of performing ensemble learning and error estimation.