Repairs to GLVQ: a new family of competitive learning schemes

@article{Karayiannis1996RepairsTG,
  title={Repairs to GLVQ: a new family of competitive learning schemes},
  author={Nicolaos B. Karayiannis and James C. Bezdek and Nikhil R. Pal and Richard J. Hathaway and Pin-I Pai},
  journal={IEEE transactions on neural networks},
  year={1996},
  volume={7 5},
  pages={1062-71}
}
First, we identify an algorithmic defect of the generalized learning vector quantization (GLVQ) scheme that causes it to behave erratically for a certain scaling of the input data. We show that GLVQ can behave incorrectly because its learning rates are reciprocally dependent on the sum of squares of distances from an input vector to the node weight vectors. Finally, we propose a new family of models-the GLVQ-F family-that remedies the problem. We derive competitive learning algorithms for each… CONTINUE READING

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