Transformational theory of feedforward neural networks

We also consider algebraic transformations on networks aimed at simplification (in the sense of removing superfluous nodes and connections within hidden layers) which preserve learning properties. Preservation of these properties is defined in terms of metrics based on the derivatives of error with respect to the weights for each stimulus. We discuss some of the properties of this learning curve.