Explanation-based neural network learning
This chapter introduces the major learning approach studied in this book: the explanation-based neural network learning algorithm (EBNN). EBNN approaches the meta-level learning problem by learning a theory of the domain. This domain theory is domain-specific. It characterizes, for example, the relevance of individual features, their cross-dependencies, or certain invariant properties of the domain that apply to all learning tasks within the domain. Obviously, when the learner has a model of such regularities, there is an opportunity to generalize more accurately or, alternatively, learn from less training data. This is because without knowledge about these regularities the learner has to learn them from scratch, which necessarily requires more training data. EBNN transfers previously learned knowledge by explaining and analyzing training examples in terms of the domain theory. The result of this analysis is a set of domain-specific shape constraints for the function to be learned at the base-level. Thus, these constraints guide the base-level learning of new functions in a knowledgeable, domain-specific way.