Partitioning strategies for modular neural networks

We observe the effects of a variety of splitting strategies for partitioning the input domain in a self-splitting modular neural network applied to the two-spiral classification problem, and assisted by a special-purpose visualization tool. The observations motivate the development of an improved strategy, consisting of a series of binary splits along the boundaries of trained areas, and a particular weight initialization strategy. The work is leading to fewer networks and better generalization for this application, when backpropagation is used.