Learning Rule for Linear Multilayer Feedforward ANN by Boosted Decision Stumps (original) (raw)

Lecture Notes in Computer Science, 2015

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 layer-wise 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.

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