Summary

Perceptron training rule guaranteed to succeed if Training examples are linearly separable Sufficiently small learning rate $\eta$

Linear unit training rule uses gradient descent Guaranteed to converge to hypothesis with minimum squared error Given sufficiently small learning rate $\eta$ Even when training data contains noise Even when training data not separable by $H$