Experiments with Supervised Fuzzy LVQ

  • Christian Thiel
  • Britta Sonntag
  • Friedhelm Schwenker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5064)

Abstract

Prototype based classifiers so far can only work with hard labels on the training data. In order to allow for soft labels as input label and answer, we enhanced the original LVQ algorithm. The key idea is adapting the prototypes depending on the similarity of their fuzzy labels to the ones of training samples. In experiments, the performance of the fuzzy LVQ was compared against the original approach. Of special interest was the behaviour of the two approaches, once noise was added to the training labels, and here a clear advantage of fuzzy versus hard training labels could be shown.

Keywords

Radial Basis Function Training Sample Training Label Fruit Data Input Label 
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 2008

Authors and Affiliations

  • Christian Thiel
    • 1
  • Britta Sonntag
    • 1
  • Friedhelm Schwenker
    • 1
  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany

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