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Recent advances in training deep (multi-layer) architectures have inspired arenaissance in neural network use. For example, deep convolutional networks arebecoming the default option for difficult tasks on large datasets, such asimage and speech recognition. However, here we show that error rates below 1%on the MNIST handwritten digit benchmark can be replicated with shallownon-convolutional neural networks. This is achieved by training such networksusing the 'Extreme Learning Machine' (ELM) approach, which also enables a veryrapid training time (~10 minutes). Adding distortions, as is common practisefor MNIST, reduces error rates even further. Our methods are also shown to becapable of achieving less than 5.5% error rates on the NORB image database. Toachieve these results, we introduce several enhancements to the standard ELMalgorithm, which individually and in combination can significantly improveperformance. The main innovation is to ensure each hidden-unit operates only ona randomly sized and positioned patch of each image. This form of random`receptive field' sampling of the input ensures the input weight matrix issparse, with about 90% of weights equal to zero. Furthermore, combining ourmethods with a small number of iterations of a single-batch backpropagationmethod can significantly reduce the number of hidden-units required to achievea particular performance. Our close to state-of-the-art results for MNIST andNORB suggest that the ease of use and accuracy of the ELM algorithm fordesigning a single-hidden-layer neural network classifier should cause it to begiven greater consideration either as a standalone method for simpler problems,or as the final classification stage in deep neural networks applied to moredifficult problems.

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Fast, simple and accurate handwritten digit classification by training  shallow neural network classifiers with the 'extreme learning machine'  algorithm