Assessing the Feasibility of Approximating Higher-Order Problem Signatures in Artificial Neural Networks With Hybrid Transfer Functions
Problem signatures are patterns that reveal a glimpse of the computational strategy most likely to be suitable for a given problem. Such a pattern could be the preferred choice of the activation and output functions for a given problem in neural networks that implement transfer functions optimization. The authors refer to these patterns as first-order signatures. Higher-order signatures capture information on a higher level, such as the likelihood of neural computational paths (i.e. connection between two or more transfer functions) used by the fittest models for specific problems. In addition, it also captures information about their weights.