Skip to Main Content
An unsupervised or nonsupervised adaptive algorithm for linear decision boundaries is applied to two pattern recognition problems: the classification of spoken words, and the classification of hand-printed characters. The term unsupervised indicates that the class identification of the input patterns is not continuously available to the adaptive system. The algorithm discussed offers two advantages for pattern recognition applications. First, the number of patterns which must be labeled with class identification is reduced. Second, the adaptive system can follow changes in the class distributions over time, due to data fluctuation or hardware degradation. These advantages are demonstrated for each of the two applications.