Learning Rule for Linear Multilayer Feedforward ANN by Boosted Decision Stumps
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.
- 1.Baig, M.M., Awais, M.M., El-Alfy, E.-S.M.: BOOSTRON: boosting based perceptron learning. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014, Part I. LNCS, vol. 8834, pp. 199–206. Springer, Heidelberg (2014) Google Scholar
- 4.Iba, W., Langley, P.: Induction of one-level decision trees. In: Proceedings of the 9th International Conference on Machine Learning, pp. 233–240 (1992)Google Scholar
- 5.Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
- 7.Rumelhart, D.E., McClelland, J.L., PDP Research Group.: Parallel Distributed Processing: Explorations in the Microstructures of Cognition, vol. 1. MIT Press, Cambridge, MA, USA (1986)Google Scholar