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Improved AURA k-Nearest Neighbour approach

By M. Weeks, V. Hodge, S. O'Keefe, J. Austin and K. Lees

Abstract

The k-Nearest Neighbour (kNN) approach is a widely-used technique for pattern classification. Ranked distance measurements to a known sample set determine the classification of unknown samples. Though effective, kNN, like most classification methods does not scale well with increased sample size. This is due to their being a relationship between the unknown query and every other sample in the data space. In order to make this operation scalable, we apply AURA to the kNN problem. AURA is a highly-scalable associative-memory based binary neural-network intended for high-speed approximate search and match operations on large unstructured datasets. Previous work has seen AURA methods applied to this problem as a scalable, but approximate kNN classifier. This paper continues this work by using AURA in conjunction with kernel-based input vectors, in order to create a fast scalable kNN classifier, whilst improving recall accuracy to levels similar to standard kNN implementations

Publisher: SPRINGER-VERLAG BERLIN
Year: 2003
OAI identifier: oai:eprints.whiterose.ac.uk:769

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