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