TY - GEN
T1 - Improved AURA k-Nearest Neighbour approach
AU - Weeks, M.
AU - Hodge, V.
AU - O'Keefe, S.
AU - Austin, J.
AU - Lees, K.
N1 - Copyright © 2003 Springer-Verlag. This is an author produced version of a chapter published in Lecture Notes in Computer Science. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.The original publication is available at http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2687&spage=663
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
M3 - Conference contribution
SN - 3-540-40211-X
VL - 2687
T3 - Lecture Notes in Computer Science
SP - 663
EP - 670
BT - ARTIFICIAL NEURAL NETS PROBLEM SOLVING METHODS, PT II
A2 - Mira, J
A2 - Alvarez, JR
PB - Springer
CY - BERLIN
T2 - 7th International Work Conference on Artificial and Natural Neural Networks
Y2 - 3 June 2003 through 6 June 2003
ER -