Improving learning rate of neural tree networks using thermal perceptrons

Abstract

A new neural network called the neural tree network (NTN) is a combination of decision trees and multi-layer perceptrons (MLP). The NTN grows the network as opposed to MLPs. The learning algorithm for growing NTNs is more efficient that standard decision tree algorithms. Simulation results have shown that the NTN is superior in performance to both decision trees and MLPs. A new NTN learning algorithm is proposed based on the thermal perceptron algorithm. It is shown that the new algorithm greatly increases the speed of learning of the NTN and attains similar classification performance as the previously used algorithm. >