SELF-ORGANIZING FEATURE MAP WITH IMPROVED PERFORMANCE BY NON-MONOTONIC VARIATION OF THE LEARNING RATE

摘要

The learning rate used for updating the weights of a self-ordering feature map is determined by a process that injects some type of perturbation into the value so that it is not simply monotonically decreased with each training epoch. For example, the learning rate may be generated according to a pseudorandom process. The result is faster convergence of the synaptic weights.