A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory

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.

• Type: Conference
• Year: 1998
• Where: NIPS
• Authors: Ping Zhou, Jim Austin, John Kennedy
• Keywords: Binary Cmm, CMM Input Requirements, Fast K-NN Classifier, NIPS 1998, NIPS 2007