Uniform Learning of Recursive Functions
inkl. 7% MwSt.
Inductive inference is concerned with formal models of learning behaviour for the analysis of general learning phenomenons. In this context, the author studies uniform inductive inference - an approach to meta-learning in the sense that, given descriptions of solvable learning problems, a meta-learner has to develop successful learners for each of them. It turns out that uniform learning is not a trivial task: the success of meta-learners strongly depends on the choice of the particular descriptions of the learning problems. The present study investigates the capabilities of meta-learners, resulting from three different formal approaches to uniform learning. The strength and weakness of uniform learning is illustrated by numerous examples; in particular, the success of specific universal methods is analysed. Finally, a formal analysis of the complexity of different learning tasks concludes the study.