A new model of associative memories network

Roberto A. Vázquez, Humberto Sossa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

An associative memory (AM) is a special kind of neural network that only allows associating an output pattern with an input pattern. However, some problems require associating several output patterns with a unique input pattern. Classical associative and neural models cannot solve this simple task. In this paper we propose a new network composed of several AMs aimed to solve this problem. By using this new model, AMs can be able to associate several output patterns with a unique input pattern. We test the accuracy of the proposal with a database of real images. We split this database of images into four collections of images and then we trained the network of AMs. During training we associate an image of a collection with the rest of the images belonging to the same collection. Once trained the network we expected to recover a collection of images by using as an input pattern any image belonging to the collection.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - ANNIIP 2007; In Conjunction with ICINCO 2007
Pages3-12
Number of pages10
StatePublished - 2007
Event3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - ANNIIP 2007; In Conjunction with ICINCO 2007 - Angers, France
Duration: 10 May 200710 May 2007

Publication series

NameProceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - ANNIIP 2007; In Conjunction with ICINCO 2007

Conference

Conference3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - ANNIIP 2007; In Conjunction with ICINCO 2007
Country/TerritoryFrance
CityAngers
Period10/05/0710/05/07

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