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Abstract for lovell_neocognitron

Paper submitted to IEEE Trans. Neural Networks


David Lovell, Tom Downs and Ah Chung Tsoi

July 1995

We describe a sequence of experiments investigating the strengths and limitations of Fukushima's neocognitron as a handwritten digit classifier. Using the results of these experiments as a foundation, we propose and evaluate improvements to Fukushima's original network in an effort to obtain recognition performance on a par with state-of-the-art digit recognition systems.

The neocognitron's performance is shown to be highly dependent on the choice of selectivity parameters and we present two methods to adjust these variables. Performance of the network under the more effective of the two selectivity adjustment techniques suggests that e network fails to exploit the features that distinguish different classes of input data. To avoid this shortcoming, the network's final layer cells were replaced by a nonlinear classifier (a multilayer perceptron) to create a hybrid architecture.

Tests of Fukushima's original system and the novel systems proposed in this paper suggest that the neocognitron is unable to achieve the rformance of existing digit classifiers due to its reliance upon the supervisor's choice of selectivity parameters and training data. ese findings pertain to Fukushima's implementation of the system and should not be seen as diminishing the practical significance of the concept of hierarchical feature extraction embodied in the neocognitron.

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