Instance-Based Learning of Credible Label Sets

  • Eyke Hüllermeier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2821)

Abstract

Even though instance-based learning performs well in practice, it might be criticized for its neglect of uncertainty: An estimation is usually given in the form of a predicted label, but without characterizing the confidence of this prediction. In this paper, we propose an instance-based learning method that allows for deriving “credible” estimations, namely set-valued predictions that cover the true label of a query object with high probability. Our method is built upon a formal model of the heuristic inference principle underlying instance-based learning.

Keywords

Precise Prediction True Label Similarity Hypothesis Query Object Incorrect Prediction 
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 2003

Authors and Affiliations

  • Eyke Hüllermeier
    • 1
  1. 1.Informatics InstituteMarburg UniversityGermany

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