Applications of neural networks
Section of Networks in practice, part of Statistical learning
Applications of neural networks
| Type | Section |
| Taught in | Networks in practice |
| Part | Statistical learning |
Steering from a road image. Pomerleau (1989) used a network, ALVINN, to steer a car. The input is a 30\times 32-pixel digitised picture of the driver’s view and the 30 outputs represent the steering direction. The output is a steering signal and a confidence. The weights are sometimes interpretable: shown at the position of the corresponding image pixel, with box size indicating magnitude and colour the sign, they reveal which parts of the road image influence the steering.
Reading handwritten ZIP codes. Handwritten-digit recognition provides a concrete reason for weight sharing. A detector for a short stroke should be useful in more than one image location. Convolutional architectures apply shared filters to local patches, then combine the resulting features. LeCun, Bottou, Bengio and Haffner (1998) describe this approach and evaluate document-recognition systems. Any quoted accuracy must specify the test population and whether uncertain cases are rejected. Accuracy among accepted cases and accuracy on every submitted digit are different quantities.
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See also
Softmax is multinomial logistic regression · What has to be settled before a step · Weight initialisation · Variance argument for weight initialisation · Batch and online learning · Preparing data for a network · Output coding, softmax, and cross-entropy · Constructing and training a network