Learning from Label Preferences

  • Eyke Hüllermeier
  • Johannes Fürnkranz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6926)

Abstract

In this paper, we review the framework of learning (from) label preferences, a particular instance of preference learning. Following an introduction to the learning setting, we particularly focus on our own work, which addresses this problem via the learning by pairwise comparison paradigm. From a machine learning point of view, learning by pairwise comparison is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We also discuss how a number of common machine learning tasks, such as multi-label classification, hierarchical classification or ordinal classification, may be addressed within the framework of learning from label preferences. We also briefly address theoretical questions as well as algorithmic and complexity issues.

Keywords

Support Vector Machine Preference Learning Weighted Vote Machine Learn Research Label Preference 
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

  • Eyke Hüllermeier
    • 1
  • Johannes Fürnkranz
    • 2
  1. 1.Philipps-Universität MarburgGermany
  2. 2.Technische Universität DarmstadtGermany

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