A learning model for multiple-prototype classification of strings

  title={A learning model for multiple-prototype classification of strings},
  author={Ram{\'o}n Alberto Mollineda},
  journal={Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.},
  pages={420-423 Vol.4}
An iterative learning method to update labeled string prototypes for a 1-nearest prototype (1-np) classification is introduced. Given a (typically reduced) set of initial string prototypes and a training set, it iteratively updates prototypes to better discriminate training samples. The update rule, which is based on the edit distance, adjusts a prototype by removing those local differences which are both frequent with respect to same-class closer training strings and infrequent with respect to… CONTINUE READING

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