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Automatic Detection of Learnability under Unreliable and Sparse User Feedback

  • Yvonne Moh
  • Wolfgang Einhäuser
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5096)

Abstract

Personalization for real-world machine-learning applications usually has to incorporate user feedback. Unfortunately, user feedback often suffers from sparsity and possible inconsistencies. Here we present an algorithm that exploits feedback for learning only when it is consistent. The user provides feedback on a small subset of the data. Based on the data representation alone, our algorithm employs a statistical criterion to trigger learning when user feedback is significantly different from random. We evaluate our algorithm in a challenging audio classification task with relevance to hearing aid applications. By restricting learning to an informative subset, our algorithm substantially improves the performance of a recently introduced classification algorithm.

Keywords

User Preference Automatic Detection User Feedback Concept Drift Classical Music 
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 2008

Authors and Affiliations

  • Yvonne Moh
    • 1
  • Wolfgang Einhäuser
    • 1
  • Joachim M. Buhmann
    • 1
  1. 1.Institute of Computational ScienceSwiss Federal Institute of Technology (ETH) Zurich 

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