Linear classifiers by window training and basis exchange

Window training, based on an extended form of stochastic approximation, offers a means of producing linear classifiers that minimize the probability of misclassification of statistically generated data. However, window training may produce a local minimum that exceeds the global minimum error rate. To overcome this defect it is useful to precede window training by perceptron training. When a significantly large set of exemplars of the data is available at the beginning of the training process, the basic exchange algorithm offers a computationally convenient alternative to the window training algorithm to achieve a locally minimum error rate