A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory
A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory
复制标题
使用二元相关矩阵存储器的高性能 k-NN 分类器
DOI：
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
J. Kennedy
中科院分区：
文献类型：
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作者：
P. Zhou;J. Austin;J. Kennedy
提出了一种基于二进制相关矩阵记忆(CMM)神经网络的新型快速k-NN分类器。为了满足坐标测量机的输入要求，开发了一种健壮的编码方法。描述了三坐标测量机的硬件实现，它的速度是目前中端工作站的200多倍，并且可以扩展到非常大的问题。在多个基准测试中，与简单的k-NN方法相比，CMM分类器的准确率降低了不到1%，软件和硬件的速度分别提高了4倍和12倍以上。
This paper presents a novel and fast k-NN classifier that is based on a binary CMM (Correlation Matrix Memory) neural network. A robust encoding method is developed to meet CMM input requirements. A hardware implementation of the CMM is described, which gives over 200 times the speed of a current mid-range workstation, and is scaleable to very large problems. When tested on several benchmarks and compared with a simple k-NN method, the CMM classifier gave less than 1% lower accuracy and over 4 and 12 times speed-up in software and hardware respectively.