Incorporating statistical techniques into empirical symbolic learning systems

Apart from connectionist approaches and genetic algorithms, for the most part the methods of inductive concept learning share the common objectives of classifying and producing predictive knowledge from observations. Although the rules produced are generally required to be intelligible and accurate, some problems arise due both to the complexity of the description languages and the noise and uncertainty in the initial observations.