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Weeks, M.; Hodge, V.; O'Keefe, S.; Austin, J.; Lees, K. (2003)
Publisher: SPRINGER-VERLAG BERLIN
Languages: English
Types: Other
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_PATTERNRECOGNITION
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.

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