Hybrid optimization of feedforward neural networks for handwritten character recognition

An extension of a feedforward neural network is presented. Although utilizing linear threshold functions and a boolean function in the second layer, signal processing within the neural network is real. After mapping input vectors onto a discretization of the input space, real valued features of the internal representation of pattern are extracted. A vectorquantizer assigns a class hypothesis to a pattern based on its extracted features and adequate reference vectors of all classes in the decision space of the output layer. Training consists of a combination of combinatorial and convex optimization. This work has been applied to a standard optical character recognition task. Results and comparison to alternative approaches are presented.