Yan H
Department of Electrical Engineering, University of Sydney, NSW, Australia.
Int J Neural Syst. 1995 Dec;6(4):417-23. doi: 10.1142/s0129065795000275.
The basic Nearest Neighbor Classifier (NNC) is often inefficient for classification in terms of memory space and computing time needed 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. In this paper, we compare the performance of the multilayer neural network and an Optimized Nearest Neighbor Classifier (ONNC) for handwritten digit recognition applications. We show that an ONNC can have the same recognition performance as an equivalent neural network classifier. The ONNC can be efficiently implemented using prototype and variable ranking, partial summation and distance triangular inequality based strategies. It requires the same memory space as, but less, training time and classification time than the neural network.
如果将所有训练样本用作原型，基本的最近邻分类器（NNC）在所需的内存空间和计算时间方面通常对于分类而言效率较低。通过使用聚类算法减少原型数量并使用特殊的神经网络模型优化原型，可以解决这些问题。在本文中，我们比较了多层神经网络和优化最近邻分类器（ONNC）在手写数字识别应用中的性能。我们表明，一个ONNC可以具有与等效神经网络分类器相同的识别性能。ONNC可以使用基于原型和变量排序、部分求和以及距离三角不等式的策略来高效实现。它需要与神经网络相同的内存空间，但训练时间和分类时间比神经网络少。
