Learning in structured domains

By and large, learning from examples in the machine learning litera-ture refers to static data types. That main stream of interest, however, has had signicant bifurcations (see e.g. the learning issues connected with syntactic and structured pattern recognition) arisen from the need to exploit the structure inherently attached to the data of some learning tasks. In this paper, I review brie y the research carried out in the last few years in the area of connectionist models in the attempt to extend the corresponding learning approaches to the case of structured domain. I give a unied picture of the adaptive computation which can be carried out on graphical objects and show that, under certain restrictions on the kind of graph to be processed, the classic learning algorithm for feedforward networks can be straightforwardly extended.