Abstract
This paper considers on-line training of feadforward neural networks. Training examples are only available sampled randomly from a given generator. What emerges in this setting is the problem of step-sizes, or learning rates, adaptation. A scheme of determining step-sizes is introduced here that satisfies the following requirements: (i) it does not need any auxiliary problem-dependent parameters, (ii) it does not assume any particular loss function that the training process is intended to minimize, (iii) it makes the learning process stable and efficient. An experimental study with the 2D Gabor function approximation is presented.
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