Department of Computer Science

Rochester Institute of Technology

pga@cs.rit.edu

A neural net with a single, two-element hidden
layer is capable of learning the identity map
on the set of *N* standard unit vector in *N*-space.
The exemplars map into points on a circle in two-space.

The usual training algorithms break down about *N* = 14,
but the author's technique of using "training wheels"
is successful for much larger values of *N*.

Bouyed with this success, we attempt to train a network to learn the identity function on a set of exemplars consisting of sums of two standard unit vectors (think of an image with two black pixels). These networks require four hidden nodes. The exemplars organize themselves into a Mobius strip in four-space. The training is, of course, accomplished using training wheels.

Such identity-function networks are generally valuable, and more so when training wheels are used in their education.

Colloquia Series page.