Maximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems

  • Juan M. Corchado
  • Emilio S. Corchado
  • Jim Aiken
  • Colin Fyfe
  • Florentino Fernandez
  • Manuel Gonzalez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2689)


CBR systems are normally used to assist experts in the resolution of problems. During the last few years researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a Maximum Likelihood Hebbian Learning-based method that automates the organisation of cases and the retrieval stage of casebased reasoning systems. The proposed methodology has been derived as an extension of the Principal Component Analysis, and groups similar cases, identifying clusters automatically in a data set in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.


Kernel Method Independent Component Analysis Case Base Reasoning Kernel Principal Component Analysis General Cost Function 
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

  • Juan M. Corchado
    • 1
  • Emilio S. Corchado
    • 4
  • Jim Aiken
    • 2
  • Colin Fyfe
    • 3
  • Florentino Fernandez
    • 5
  • Manuel Gonzalez
    • 1
  1. 1.Dept. InformáticaUniversity of VigoOurenseSpain
  2. 2.Dept. de Informática y AutomáticaUniversity of SalamancaSalamancaSpain
  3. 3.Dept. de Ingeniería CivilUniversity of BurgosBurgosSpain
  4. 4.Plymouth Marine LaboratoryPlymouthUK
  5. 5.Computing and Information System DeptUniversity of PaisleyPaisleyUK

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