Data Set Used
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)
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)
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.
Editors: Ronny Kohavi and Foster Provost " So much research, so few good products " Abstract. 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… (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)