Learning rule for linear multilayer feedforward ann by boosted decision stumps

Abstract

A novel method for learning a linear multilayer feedforward artificial neural network (ANN) by using ensembles of boosted decision stumps is presented. Network parameters are adapted through a layerwise iterative traversal of neurons with weights of each neuron learned by using a boosting based ensemble and an appropriate reduction. Performances of several neural network models using the proposed method are compared for a variety of datasets with networks learned using three other algorithms, namely Perceptron learning rule, gradient decent back propagation algorithm, and Boostron learning.