Abstract. We present a learning algorithm for feedforward neural networks that is based on Kolmogorov theorem concerning composition of ndimensional continuous function from one-dimensional continuous functions. A thorough analysis of the algorithm time complexity is presented together with serial and parallel implementation examples.
Abstract.A thorough analysis of theoretical and computational properties of kolmogorov learning algorithm for feedforward neural networks lead us to proposal of efficient sequential and parallel implementation. A novel approach to parallelization which combines our previous results in order to achieve higher parallel speedup is presented.
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