Soft nearest prototype classification

@article{Seo2003SoftNP,
  title={Soft nearest prototype classification},
  author={Sambu Seo and Mathias Bode and Klaus Obermayer},
  journal={IEEE transactions on neural networks},
  year={2003},
  volume={14 2},
  pages={390-8}
}
We propose a new method for the construction of nearest prototype classifiers which is based on a Gaussian mixture ansatz and which can be interpreted as an annealed version of learning vector quantization (LVQ). The algorithm performs a gradient descent on a cost-function minimizing the classification error on the training set. We investigate the properties of the algorithm and assess its performance for several toy data sets and for an optical letter classification task. Results show 1) that… CONTINUE READING
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