Improved AURA k-Nearest Neighbour approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Publication details

Title of host publicationARTIFICIAL NEURAL NETS PROBLEM SOLVING METHODS, PT II
DatePublished - 2003
Pages663-670
Number of pages8
PublisherSPRINGER-VERLAG BERLIN
Place of PublicationBERLIN
EditorsJ Mira, JR Alvarez
Volume2687
Original languageEnglish
ISBN (Print)3-540-40211-X

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag

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.

Bibliographical note

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

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