Incremental distributed classifier building
Emmanuel Stocker, Arnaud Ribert, Y. Lecourtier, Asmae Ennaji · 1996
In this paper we present a scheme of classification based on a particular processing element (neuron) called yprel. The main characteristics of the approach are: (1) an yprel classifier is a set of yprels networks, each network being associated with a particular class; (2) the learning is supervised and conducted class by class; (3) the structure of the network is not a priori chosen, but is determined step by step during the learning process; (4) the learning process is incremental: each network improves its own learning base with the errors of the previous test; (5) networks cooperate: each network can use the outputs of the previously built networks. Preliminary results are given on a well-known classification task (recognition of typographic characters).