back-propagation

(Or "backpropagation") A learning algorithm for modifying a

feed-forward neural network which minimises a continuous

"error function" or "objective function."

Back-propagation is a "gradient descent" method of training

in that it uses gradient information to modify the network

weights to decrease the value of the error function on

subsequent tests of the inputs. Other gradient-based methods

from numerical analysis can be used to train networks more

efficiently.

Back-propagation makes use of a mathematical trick when the

network is simulated on a digital computer, yielding in just

two traversals of the network (once forward, and once back)

both the difference between the desired and actual output, and

the derivatives of this difference with respect to the

connection weights.