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Superset Learning Based on Generalized Loss Minimization

  • Eyke HüllermeierEmail author
  • Weiwei Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9285)

Abstract

In standard supervised learning, each training instance is associated with an outcome from a corresponding output space (e.g., a class label in classification or a real number in regression). In the superset learning problem, the outcome is only characterized in terms of a superset—a subset of candidates that covers the true outcome but may also contain additional ones. Thus, superset learning can be seen as a specific type of weakly supervised learning, in which training examples are ambiguous. In this paper, we introduce a generic approach to superset learning, which is motivated by the idea of performing model identification and “data disambiguation” simultaneously. This idea is realized by means of a generalized risk minimization approach, using an extended loss function that compares precise predictions with set-valued observations. As an illustration, we instantiate our meta learning technique for the problem of label ranking, in which the output space consists of all permutations of a fixed set of items. The label ranking method thus obtained is compared to existing approaches tackling the same problem.

Keywords

Training Instance Output Space Empirical Risk Minimization Complete Ranking Neighbor Approach 
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 International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of PaderbornPaderbornGermany
  2. 2.Amazon Inc.BerlinGermany

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