Prototyping structural descriptions: An inductive learning approach

Abstract

In structural pattern recognition, a common problem is the difficulty of constructing classification models or roles from a set of examples. This paper illustrates a method for generating class descriptions from a training set by using inductive logic programming, and in particular an inductive learning algorithm based on the FOIL system. 12ae goal of the method is to find descriptions which are general, i.e. are successfully applicable to recognize objects different from the ones in the training set, while preserving their discrimination ability. The application of the method to a difficult real-world problem (handprinted character recognition) is presented, proposing a hierarchical description and classification scheme in order to reduce the complexity of the task to a level which can be profitably handled by this kind of learning methodologies.