Nearest neighbor pattern classification perceptrons
Owen J. Murphy · Proceedings of the IEEE · 1990
A three-layer perceptron that uses the nearest-neighbor pattern classification rule is presented. This neural network is of interest because it is designed specifically for the set of training patterns, and incorporating of the training of the network into the design eliminates the need for the use of training algorithms. The technique therefore provides an alternative to the limitations and unpredictability (such as having too many, too few, or inappropriate training patterns) of the known training techniques. Since the nearest-neighbor classification rule is used, the network is capable of forming arbitrarily complex decision regions. The design and training of the network can be completed in polynomial time, whereas it has been shown that training a neural network is an NP-complete problem.>