Optimal learning rates for each pattern and neuron in gradient descent training of multilayer perceptrons

Proposes optimal learning rates in gradient descent training of multilayer perceptrons, which are a separate learning rate for weights associated with each neuron and a separate one for assigning virtual hidden targets associated with each training pattern. That is, a hidden weight vector has two optimal learning rates, one for assigning virtual hidden targets and the other for minimizing a hidden error function proposed in the paper. Effectiveness of the proposed error function was demonstrated for handwritten digit recognition and an isolated-word recognition tasks and very fast learning convergence was obtained