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Combining additive input noise annealing and pattern transformations for improved handwritten character recognition
Authors
Juan Manuel Alonso Weber
María Araceli Sanchis de Miguel
María Paz Sesmero Lorente
Publication date
15 December 2014
Publisher
'Elsevier BV'
Doi
Cite
Abstract
Two problems that burden the learning process of Artificial Neural Networks with Back Propagation are the need of building a full and representative learning data set, and the avoidance of stalling in local minima. Both problems seem to be closely related when working with the handwritten digits contained in the MNIST dataset. Using a modest sized ANN, the proposed combination of input data transformations enables the achievement of a test error as low as 0.43%, which is up to standard compared to other more complex neural architectures like Convolutional or Deep Neural Networks. © 2014 Elsevier Ltd. All rights reserved.This research reported has been supported by the Spanish MICINN under projects TRA2010-20225-C03-01, TRA 2011-29454-C03-02, and TRA 2011-29454-C03-03
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Last time updated on 27/10/2022