Metric Learning for Prototype-Based Classification

@inproceedings{Biehl2009MetricLF,
  title={Metric Learning for Prototype-Based Classification},
  author={Michael Biehl and Barbara Hammer and Petra Schneider and Thomas Villmann},
  booktitle={Innovations in Neural Information Paradigms and Applications},
  year={2009}
}
In this chapter, one of the most popular and intuitive prototype-based classification algorithms, learning vector quantization (LVQ), is revisited, and recent extensions towards automatic metric adaptation are introduced. Metric adaptation schemes extend LVQ in two aspects: on the one hand a greater flexibility is achieved since the metric which is essential for the classification is adapted according to the given classification task at hand. On the other hand a better interpretability of the… CONTINUE READING
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Ex - ploration of MassSpectrometric Data in Clinical Proteomics Using Learning Vector Quantization Methods

  • F.-M. Schleif, B. Hammer, M. Kostrzewa, T. Villmann
  • Briefings in Bioinformatics
  • 2007

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