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Kernel Computation in Morphological Bidirectional Associative Memories

  • Gonzalo Urcid
  • Gerhard X. Ritter
  • Laurentiu Iancu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

Morphological associative memories (MAMs) use a lattice algebra approach to store and recall pattern associations. The lattice matrix operations endow MAMs with properties that are completely different than those of traditional associative memory models. In the present paper, we focus our attention to morphological bidirectional associative memories (MBAMs) capable of storing and recalling non-boolean patterns degraded by random noise. The notions of morphological strong independence (MSI), minimal representations, and kernels are extended to provide the foundation of bidirectional recall when dealing with noisy inputs. For arbitrary pattern associations, we present a practical solution to compute kernels in MBAMs by induced MSI.

Keywords

Associative Memory Kernel Computation Minimal Representation Pattern Association Memory Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Gonzalo Urcid
    • 1
  • Gerhard X. Ritter
    • 2
  • Laurentiu Iancu
    • 2
  1. 1.Optics DepartmentINAOETonantzintlaMexico
  2. 2.CISE DepartmentUniversity of FloridaGainesvilleUSA

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