A Geometrical Approach to Constructing Neural Networks for Perfect Training-set Classification
The method constructs a neural network to perfectly classify training data without regularization by using a geometrical approach. It builds two hidden layers based on recursively partitioning the data with separating hyperplanes, mapping these splits directly to neurons. This results in compact models that achieve perfect classification on the training set.