  Considered in this short note is the design of output layer nodes of
feedforward neural networks for solving multi-class classification problems
with r (bigger than or equal to 3) classes of samples. The common and
conventional setting of output layer, called "one-to-one approach" in this
paper, is as follows: The output layer contains r output nodes corresponding to
the r classes. And for an input sample of the i-th class, the ideal output is 1
for the i-th output node, and 0 for all the other output nodes. We propose in
this paper a new "binary approach": Suppose r is (2^(q minus 1), 2^q] with q
bigger than or equal to 2, then we let the output layer contain q output nodes,
and let the ideal outputs for the r classes be designed in a binary manner.
Numerical experiments carried out in this paper show that our binary approach
does equally good job as, but uses less output nodes than, the traditional
one-to-one approach.
