| Summary: | Leaming in neural networks has attracted considerable interest in recent years. Our focus is
on learning in single hidden layer feedforward networks which is posed as a search in the
network parameter space for a network that minimizes an additive error function of
statistically independent examples. In this contribution, we review first the class of single
hidden layer feedforward networks and characterize the learning process in such networks
from a statistical point of view. Then we describe the backpropagation procedure, the leading
case of gradient descent learning algorithms for the class of networks considered here, as
well as an efficient heuristic modification. Finally, we analyse the applicability of these
learning methods to the problem of predicting interregional telecommunication flows.
Particular emphasis is laid on the engineering judgment, first, in choosing appropriate
values for the tunable parameters, second, on the decision whether to train the network by
epoch or by pattern (random approximation), and, third, on the overfitting problem. In
addition, the analysis shows that the neural network model whether using either epoch-based
or pattern-based stochastic approximation outperforms the classical regression approach to
modelling telecommunication flows. (authors' abstract) === Series: Discussion Papers of the Institute for Economic Geography and GIScience
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