  We propose a new method for learning deep neural network models that is based
on a greedy learning approach: we add one basis function at a time, and a new
basis function is generated as a non-linear activation function applied to a
linear combination of the previous basis functions. Such a method (growing deep
neural network by one neuron at a time) allows us to compute much more accurate
approximants for several model problems in function approximation.
