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Prototype-Based Classification of Dissimilarity Data

  • Barbara Hammer
  • Bassam Mokbel
  • Frank-Michael Schleif
  • Xibin Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7014)

Abstract

Unlike many black-box algorithms in machine learning, prototype-based models offer an intuitive interface to given data sets, since prototypes can directly be inspected by experts in the field. Most techniques rely on Euclidean vectors such that their suitability for complex scenarios is limited. Recently, several unsupervised approaches have successfully been extended to general, possibly non-Euclidean data characterized by pairwise dissimilarities. In this paper, we shortly review a general approach to extend unsupervised prototype-based techniques to dissimilarities, and we transfer this approach to supervised prototype-based classification for general dissimilarity data. In particular, a new supervised prototype-based classification technique for dissimilarity data is proposed.

Keywords

Vector Quantization Learn Vector Quantization Relevance Vector Machine Stochastic Gradient Descent Musical Piece 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Barbara Hammer
    • 1
  • Bassam Mokbel
    • 1
  • Frank-Michael Schleif
    • 1
  • Xibin Zhu
    • 1
  1. 1.CITEC centre of excellenceBielefeld UniversityBielefeldGermany

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