METALA: A meta-learning architecture

Abstract

Meta-learning has been accepted, in the last five years, as a proper machine learning research field. In this concrete area of interest, the way in which different theories, each one produced either with the same algorithm or with many of them, are merged to produce a more accurate model has been the main topic. Now, new emerging techniques got more to do with inductive meta-learning. It is the process of learning from others learning experiences. This kind of learning imposes severe requisites, from the point of view of the software system that would support it. The purpose of this work is to show a software architecture for this type of learning. The architecture will give recommendations for building a system of this kind, that has to tackle with very precise but difficult problems at a time. © Springer-Verlag 2001.