A Soft-Competitive Splitting Rule for Adaptive Tree-Structured Neural Networks

An algorithm for generating tree structured neural networks using a soft-competitive recursive partitioning rule is described. It is demonstrated that the algorithm grows robust, honest estimators. Preliminary results on a 10 class, 240 dimensional OCR classification task are presented which show that the tree out-performs backpropagation. Arguments are made which suggest why this should be the case. The connection of the soft-competitive splitting rule to the twoing rule is described.... Soft-competition, CART, Neural networks