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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Weeks, M., Hodge, V., O’Keefe, S., Austin, J., & Lees, K. (2003). Improved AURA k-nearest neighbour approach. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2687, 663–670. https://doi.org/10.1007/3-540-44869-1_84
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