A Genetic Algorithm for Learning Weights in a Similarity Function

  • Y. Wang
  • N. Ishii
Conference paper


One large problem when employing a similarity function to measure the similarities between new and prior cases is to determine the weights of the features. This paper proposes a new method of learning weights using a genetic algorithm based on the similarity information of given examples. This method is suitable for both linear and nonlinear similarity functions. Our experimental results show the computational efficiency of the proposed approach.


Genetic Algorithm Weight Vector Similarity Function Similarity Information Analogical Reasoning 
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 Wien 1998

Authors and Affiliations

  • Y. Wang
    • 1
  • N. Ishii
    • 1
  1. 1.Department of Intelligence and Computer ScienceNagoya Institute of TechnologyGokiso-cho, syowa-ku, Nagoya 466Japan

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