A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory
Abstract
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 I % lower accuracy and over 4 and 12 times speed-up in software and hardware respectively.
Cite
Text
Zhou et al. "A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory." Neural Information Processing Systems, 1998.Markdown
[Zhou et al. "A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/zhou1998neurips-high/)BibTeX
@inproceedings{zhou1998neurips-high,
title = {{A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory}},
author = {Zhou, Ping and Austin, Jim and Kennedy, John},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {713-722},
url = {https://mlanthology.org/neurips/1998/zhou1998neurips-high/}
}