Abstract
Conventional artificial neural networks and convolutional neural networks perform well on the task of automatic handwriting recognition. But, they suffer from long training times and their complex nature. An alternative learning algorithm called Extreme Learning Machine overcomes these shortcomings by determining the weights of a neural network analytically. In this paper, a novel classifier based on Extreme Learning Machine is proposed that achieves competitive accuracy results while keeping training times low. This classifier is called multilayer ensemble Extreme Learning Machine. The novel classifier is evaluated against traditional backpropagation and Extreme Learning Machine on the well-known MNIST dataset. Possible future work on parallel Extreme Learning Machine is shown up.
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Ghodrati Noushahr, H., Ahmadi, S., Casey, A. (2015). Fast Handwritten Digit Recognition with Multilayer Ensemble Extreme Learning Machine. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXII. SGAI 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-25032-8_5
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DOI: https://doi.org/10.1007/978-3-319-25032-8_5
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