One Shot Associative Memory Method for Distorted Pattern Recognition

In this paper, we present a novel associative memory approach for pattern recognition termed as Distributed Hierarchical Graph Neuron (DHGN). DHGN is a scalable, distributed, and one-shot learning pattern recognition algorithm which uses graph representations for pattern matching without increasing...

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Bibliographic Details
Published inAI 2007: Advances in Artificial Intelligence Vol. 4830; pp. 705 - 709
Main Authors Khan, Asad I., Amin, Anang Hudaya Muhamad
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2007
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
Associative Memory
Data mining
Mathematical theory of computation
One-Shot Learning
Pattern Recognition
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ISBN9783540769262
3540769269
ISSN0302-9743
1611-3349
DOI10.1007/978-3-540-76928-6_79

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Summary:In this paper, we present a novel associative memory approach for pattern recognition termed as Distributed Hierarchical Graph Neuron (DHGN). DHGN is a scalable, distributed, and one-shot learning pattern recognition algorithm which uses graph representations for pattern matching without increasing the computation complexity of the algorithm. We have successfully tested this algorithm for character patterns with structural and random distortions. The pattern recognition process is completed in one-shot and within a fixed number of steps.
ISBN:9783540769262
3540769269
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-76928-6_79