Characterization of network responses to known, unknown, and ambiguous inputs

Neural networks typically classify patterns by mapping the input feature space to the corners of the m-dimensional unit hypercube where m is the number of output classes. When classifier networks of graded threshold neurons are presented with patterns that are strong, ambiguous, or unknown, characteristic responses are emitted. A second tier network can be used to characterize the decision of the classifier network. By using a class count-independent mapping as an intermediary, a training set of unknowns can be generated for training the second tier. The two tiered approach is described and the internal function of the second tier is analyzed