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OFAI-TR-93-16 ( 50kB g-zipped PostScript file)

Avoiding Noise Fitting in a FOIL-like Learning Algorithm

Johannes Fürnkranz

The research reported in this paper describes FOSSIL, an ILP system that uses a search heuristic based on statistical correlation. This algorithm implements a new method for learning useful concepts in the presence of noise. In contrast to FOIL's stopping criterion which allows theories to grow in complexity as the size of the training sets increase, we propose a new stopping criterion that is independent of the number of training examples. Instead, FOSSIL's stopping criterion depends on a search heuristic that estimates the utility of literals on a uniform scale.

Citation: Fürnkranz J.: Avoiding Noise Fitting in a FOIL-like Learning Algorithm, Proc. IJCAI-93 workshop on Inductive Logic Programming, Chambery, France, August 1993.