Generic probabilistic prototype based classification of vectorial and proximity data

Frank-michael Schleif

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
252 Downloads (Pure)


In supervised learning probabilistic models are attractive to define discriminative models in a rigid mathematical framework. More recently, prototype approaches, known for compact and efficient models, were defined in a probabilistic setting, but are limited to metric vectorial spaces. Here we propose a generalization of the discriminative probabilistic prototype learning algorithm for arbitrary proximity data, widely applicable to a multitude of data analysis tasks. We extend the algorithm to incorporate adaptive distance measures, kernels and non-metric proximities in a full probabilistic framework.
Original languageEnglish
Pages (from-to)208-216
Early online date17 Dec 2014
Publication statusPublished - 22 Apr 2015


  • Prototype learning
  • Proximity data
  • Probabilistic model
  • Fuzzy label
  • Matrix learning
  • Classification


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