Symbolic vs. connectionist learning: an experimental comparison in a structured domain

Abstract

During the last two decades, the attempts to find effective solutions to the problem of learning any kind of structured information have been splitting the scientific community. A "holy war" has been fought between the advocates of a symbolic approach to learning and the advocates of a connectionist approach. One of the most repeated claims of the symbolic party has been that symbolic methods are able to cope with structured information while connectionist ones are not. However, in the last few years, the possibility of employing connectionist methods for structured data has been widely investigated and several approaches have been proposed. 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 graphs by defining the proper inference operators, as graph generalization and graph specialization, and obtains general and consistent prototypes with a low computational cost with respect to other symbolic learning systems. The proposed algorithm is compared with a recent connectionist method for learning structured data (P. Frasconi et al., 1998), with reference to a problem of handwritten character recognition from a standard database on the Web. The orthogonality of the two approaches strongly suggests their combination in a multiclassifier system so as to retain the strengths of both of them, while overcoming their weaknesses. The results on an experimental case study demonstrated that the adoption of a parallel combination scheme of the two algorithms could improve the recognition performance by about 10 percent. A truce or an alliance between the symbolic and the connectionist worlds?.