A low cost pattern classifier based on coding techniques

A general method for designing two-layer neural networks which can be used as pattern classifiers is presented. The method is based on coding techniques. It is independent of the number of pattern elements and results in a minimal-cost implementation for correcting and detecting random errors from black to white or from white to black. It can also detect all the unidirectional errors from white to black and all the unidirectional errors from black to white, preventing incorrectly recognized input images. It reduces the number of connections needed by the resulting neural networks by more than 80%, as is shown in an example. compared with the other currently available methods