An evaluation of standard retrieval algorithms and a binary neural approach

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we evaluate a selection of data retrieval algorithms for storage efficiency, retrieval speed and partial matching capabilities using a large Information Retrieval dataset. We evaluate standard data structures, for example inverted file lists and hash tables, but also a novel binary neural network that incorporates: single-epoch training, superimposed coding and associative matching in a binary matrix data structure. We identify the strengths and weaknesses of the approaches. From our evaluation, the novel neural network approach is superior with respect to training speed and partial match retrieval time. From the results, we make recommendations for the appropriate usage of the novel neural approach. (C) 2001 Elsevier Science Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)287-303
Number of pages16
JournalNeural Networks
Volume14
Issue number3
DOIs
Publication statusPublished - Apr 2001

Bibliographical note

Copyright © 2001 Elsevier Science B.V. This is an author produced version of a paper published in Neural Networks. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.

Keywords

  • information retrieval algorithm
  • binary neural network
  • correlation matrix memory
  • word-document association
  • partial match
  • storage efficiency
  • speed of training
  • speed of retrieval
  • PARTIAL-MATCH RETRIEVAL

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