Incremental Training of a 2 Layer Network


Gradient boosting for convex objectives has had a rich history and literature with provable guarantees for many years now. The same cannot be said for the workings of a neural network, while the class of neural networks is a set of incredibly powerful models, which can approximate complex function mappings. In this project, we make an attempt to combine the two approaches with a boosted model as a warm start for a single layer neural network, with provable convergence guarantees. We also see how gradient boosting on single node single hidden layer network essentially corresponds to sequential training of hidden layer nodes, and therefore can be used as a starting point for application of the backpropagation scheme for better results. Among these, we also look at the convergence analysis of functional gradient descent, which is used to train the weak learners, or nodes in our case, and empirical results received thereafter. Link