Neural Decision Trees

Understand

In this paper we propose a synergistic melting of neural networks and decision trees (DT) we call neural decision trees (NDT).

• NDT is an architecture a la decision tree where each splitting node is an independent multilayer perceptron allowing oblique decision functions or arbritrary nonlinear decision function if more than one layer is used.
• This way, each MLP can be seen as a node of the tree.
• We then show that with the weight sharing asumption among those units, we end up with a Hashing Neural Network (HNN) which is a multilayer perceptron with sigmoid activation function for the last layer as opposed to the standard softmax.