C++ Neural Networks and Fuzzy Logic: Preface

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Image recognition. Le Cun reported a backpropagation network with three hidden layers that

could recognize handwritten postal zip codes. He used a 16×16 array of pixel to represent each

handwritten digit and needed to encode 10 outputs, each of which represented a digit from 0 to 9. One

interesting aspect of this work is that the hidden layers were not fully connected. The network was set

up with blocks of neurons in the first two hidden layers set up as feature detectors for different parts

of the previous layer. All the neurons in the block were set up to have the same weights as those from

the previous layer. This is called weight sharing. Each block would sample a different part of the

previous layer’s image. The first hidden layer had 12 blocks of 8×8 neurons, whereas the second

hidden layer had 12 blocks of 4×4 neurons. The third hidden layer was fully connected and consisted

C++ Neural Networks and Fuzzy Logic:Preface

Applications

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of 30 neurons. There were 1256 neurons. The network was trained on 7300 examples and tested on

2000 cases with error rates of 1% on training set and 5% on the test set.

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C++ Neural Networks and Fuzzy Logic:Preface

Applications

295

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