  We study the online dynamics of learning in fully connected soft committee
machines in the student-teacher scenario. The locally optimal modulation
function, which determines the learning algorithm, is obtained from a
variational argument in such a manner as to maximise the average generalisation
error decay per example. Simulations results for the resulting algorithm are
presented for a few cases. The symmetric phase plateaux are found to be vastly
reduced in comparison to those found when online backpropagation algorithms are
used. A discussion of the implementation of these ideas as practical algorithms
is given.
