Perceptron Rule Learning

Where wi is the weight from input i to perceptron node, c is the learning rate, tj is the target for the current instance, z is the current output, and xi is ith input
Least perturbation principle
Only change weights if there is an error
small c rather than changing weights sufficient to make current pattern correct
Scale by xi
Create a perceptron node with n inputs
Iteratively apply a pattern from the training set and apply the perceptron rule
Each iteration through the training set is an epoch
Continue training until total training set error ceases to improve
Perceptron Convergence Theorem: Guaranteed to find a solution in finite time if a solution exists