Hodge Victoria J, Lees Ken J, Austin James L
Advanced Computer Architecture Group, Department of Computer Science, University of York, Heslington, York YO10 5DD, UK.
Neural Netw. 2004 Apr;17(3):441-58. doi: 10.1016/j.neunet.2003.11.008.
This paper evaluates a novel k-nearest neighbour (k-NN) classifier built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and numeric data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall a candidate set of matching records, which are then processed by a conventional k-NN approach to determine the k-best matches. We compare various configurations of the binary approach to a conventional approach for memory overheads, training speed, retrieval speed and retrieval accuracy. We demonstrate the superior performance with respect to speed and memory requirements of the binary approach compared to the standard approach and we pinpoint the optimal configurations.
本文评估了一种基于二元神经网络构建的新型k近邻（k-NN）分类器。二元神经方法使用鲁棒编码将标准序数、分类和数值数据集映射到二元神经网络上。二元神经网络利用高速模式匹配来召回匹配记录的候选集，然后通过传统的k-NN方法对其进行处理以确定k个最佳匹配。我们比较了二元方法的各种配置与传统方法在内存开销、训练速度、检索速度和检索准确性方面的差异。我们证明了与标准方法相比，二元方法在速度和内存需求方面具有卓越性能，并确定了最优配置。
