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Thresholded Learning Matrix for Efficient Pattern Recalling

  • Mario Aldape-Pérez
  • Israel Román-Godínez
  • Oscar Camacho-Nieto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)

Abstract

The Lernmatrix, which is the first known model of associative memory, is a heteroassociative memory that can easily work as a binary pattern classifier if output patterns are appropriately chosen. However, this mathematical model undergoes fundamental patterns misclassification whenever crossbars saturation occurs. In this paper, a novel algorithm that overcomes Lernmatrix weaknesses is proposed. The crossbars saturation occurrence is solved by means of a dynamic threshold value which is computed for each recalled pattern. The algorithm applies the dynamic threshold value over the ambiguously recalled class vector in order to obtain a sentinel vector which is used for uncertainty elimination purposes. The efficiency and effectiveness of our approach is demonstrated through comparisons with other methods using real-world data.

Keywords

Associative Memories Dynamic Threshold Lernmatrix Pattern Classification Supervised Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mario Aldape-Pérez
    • 1
  • Israel Román-Godínez
    • 1
  • Oscar Camacho-Nieto
    • 1
  1. 1.Center for Computing Research, CIC, National Polytechnic Institute,IPNMexico CityMexico

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