Neural network initialization by combined classifiers

If a set of linear classifiers in the same feature spaces is combined by a linear output classifier and if each of these classifiers has a sigmoid output-function then this set of classifiers has the same architecture as a feedforward neural network. A combined set of classifiers, however is trained in an entirely different way. In this paper it is shown that it can be advantageous to use such a set as an initialization for a neural network.