  This paper discusses the notion of generalization of training samples over
long distances in the input space of a feedforward neural network. Such a
generalization might occur in various ways, that differ in how great the
contribution of different training features should be.
  The structure of a neuron in a feedforward neural network is analyzed and it
is concluded, that the actual performance of the discussed generalization in
such neural networks may be problematic -- while such neural networks might be
capable for such a distant generalization, a random and spurious generalization
may occur as well.
  To illustrate the differences in generalizing of the same function by
different learning machines, results given by the support vector machines are
also presented.
