A comparison of multi-layer neural networks and optimized nearest neighbor classifiers for handwritten digit recognition

The basic nearest neighbor classifier (NNC) is often inefficient for classification in terms of memory space and computing time if all training samples are used as prototypes. These problems can be solved by reducing the number of prototypes using clustering algorithms and optimizing the prototypes using a special neural network model. The author compares the performance of the multi-layer neural network and an optimized nearest neighbor classifier (ONNC). It is shown that an ONNC can have the same recognition performance and the same memory requirement as but need less training and classification time than an equivalent neural network.<
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