A neural network string matcher

Authors:
Abdolreza Mirzaei;Hamidreza Zaboli;Reza Safabakhsh
Affiliations:
Department of Computer Engineering, Amirkabir University, Tehran, Iran;Department of Computer Engineering, Amirkabir University, Tehran, Iran;Department of Computer Engineering, Amirkabir University, Tehran, Iran
Venue:
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Year:
2007

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Abstract

The aim of this work is to code the string matching problem as an optimization task and carrying out this optimization problem by means of a Hopfield neural network. The proposed method uses TCNN, a Hopfield neural network with decaying self-feedback, to find the best-matching (i.e., the lowest global distance) path between an input and a template. The proposed method is more than 'exact' string matching. For example wild character matches as well as character that never match may be used in either string. As well it can compute edit distance between the two strings. It shows a very good performance in various string matching tasks.