A Classical Algorithm for Avoiding Local Minima

Conventional methods of supervised learning are inevitably faced with the problem of local minima; evidence is presented that conjugate gradient and quasi-Newton techniques are particularly susceptible to being trapped in sub-optimal solutions. A new classical technique is presented which by the use of a homotopy on the range of the target outputs allows supervised learning methods to find a global minimum of the error function in almost every case.