Data augmentation of a handwritten character dataset for a Convolutional Neural Network and integration into a Bayesian Linear Framework
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2016-06-10
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en
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Abstract
Convolutional neural networks are often used for image recognition. They
have, for example, achieved the lowest error rate (0.23 percent) for the MNIST
database up until now [6]. The use of receptive elds also makes them similar
to how the human visual cortex works. The task at hand is to use convolutional
neural networks to nd optimizations for brainreading research [21]. The goal
is to nd these optimizations through using di erent preprocessing methods on
the handwritten character dataset [27] and testing which method results in the
highest classi cation accuracy for the convolutional neural networks. An optimization
is achieved by using the most e cient data augmentation methods
from the convolutional neural networks to preprocess the prior of the Bayesian
linear framework.
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Faculteit der Sociale Wetenschappen