Evaluation of gradient descent learning algorithms with adaptive and local learning rate for recognising hand-written numerals.
Matteo Giudici, Filippo Queirolo, Maurizio Valle · The European Symposium on Artificial Neural Networks · 2002
Gradient descent learning algorithms, namely Back Propagation (BP), can significantly increase the classification performance of Multi Layer Perceptrons adopting a local and adaptive learning rate management approach. In this paper, we present the comparison of the performance on hand-written characters classification of two BP algorithms, implementing fixed and adaptive learning rate. The results show that the validation error and average number of learning iterations are lower for the adaptive learning rate BP algorithm.