Learning profile in routing: comparison between relevance and gradient back-propagation

Compares two learning profile strategies in an information routing task: relevance backpropagation and gradient backpropagation. Gradient backpropagation is a learning approach used in multilayered neural networks in general. The convergence of the gradient backpropagation algorithm is still to be discussed, but we show its convergence in the majority of cases. Relevance backpropagation is a relevance feedback method used in our connectionist model called Mercure. Experiments carried out on Amaryllis documents showed the effectiveness of both methods, with a slight benefit for the relevance backpropagation strategy.