•Perceptron learning is a gradient descent search through the space of possible weights.
•Each training example provides an "error
surface" for weights. Learning rule runs weights downhill with learning rate a as step size.
•For linearly
separable functions, there are no local minima, and guaranteed
to converge if learning rate a not too
high (overshoot)
•Summary: Very effective for very simple representable functions.
