Abstract:

Recent theoretical advances in the learning of deep artificial neural networks have made it possible to overcome a vanishing gradient problem. This limitation has been overcome using a pre-training step, where deep belief networks formed by the stacked Restricted Boltzmann Machines perform unsupervised learning. Once a pre-training step is done, network weights are fine-tuned using regular error back propagation while treating network as a feed-forward net. In the current paper we perform the comparison of described approach and commonly used classification approaches on some well-known classification data sets from the UCI repository as well as on one mid-sized proprietary data set.

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