Partially affine invariant training using dense transform matrices

The concept of equivalent networks is reviewed as a method for testing algorithms for affine invariance. Partial affine invariance is defined and introduced to first order training through the development of linear transforms of the hidden layer´s net function vector. The resulting two-step training algorithm has convergence properties that are comparable to Levenberg-Marquardt, but with fewer multiplies per iteration.