Perceptron Learning algorithm and convergence theorem

Classic Perceptron Learning Rule:

If the classification is correct, don't change the weights
Suppose the classification is incorrect
If the correct response is 1, but the output is 0

w -> w+ c f (c is a positive fraction)

This makes w.f bigger.

If the correct response is 0, but the output is 1

w -> w - cf

This makes w.f smaller.