Boosting first-order learning

Abstract

Several empirical studies have confirmed that boosting classifier- learning systems can lead to substantial improvements in predictive accuracy. This paper reports early experimental results from applying boosting to FFOIL, a first-order system that constructs definitions of functional relations. Although the evidence is less convincing than that for propositional-level learning systems, it suggests that boosting will also prove beneficial for first-order induction