Prototyping Structural Shape Descriptions by Inductive Learning

In this paper, a novel algorithm for learning structured descriptions, ascribable to the category of symbolic techniques, is proposed. It faces the problem directly in the space of the graphs, by defining the proper inference operators, as graph generalization and graph specialization, and obtains general and coherent prototypes with a low computational cost with respect to other symbolic learning systems. The proposed algorithm is tested with reference to a problem of handwritten character recognition from a standard database.