Building feedforward neural networks with random weights for large scale datasets

Abstract

With the explosive growth in size of datasets, it becomes more significant to develop effective learning schemes for neural networks to deal with large scale data modelling. This paper proposes an iterative approximate Newton-type learning algorithm to build neural networks with random weights (NNRWs) for problem solving, where the whole training samples are divided into some small subsets under certain assumptions, and each subset is employed to construct a local learner model for integrating a unified classifier. The convergence of the output weights of the unified learner model is given. Experimental results on UCI datasets with comparisons demonstrate that the proposed algorithm is promising for large scale datasets.