Does snooping help?

V. V. V'yugin

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


One of the most efficient way of improving the performance of learning algorithms is "snooping", i.e. using some information about the data to be predicted for choosing the parameters of the learning algorithm or the learning algorithm itself. Allowing different degrees of snooping α makes it possible to attain a better performance LossP(x) of a prediction strategy P on the given data set x. We study the "snooping curves" Lx(α)=infK(P)≤αLossP(x), where K(P) is the Kolmogorov complexity of the prediction strategy P. We prove that every non-increasing function can be approximated with arbitrary precision by some snooping function Lx. Our framework is that of on-line prediction; for simplicity we assume that sequences x are binary.

Original languageEnglish
Pages (from-to)407-415
Number of pages9
JournalTheoretical Computer Science
Issue number1-2
Publication statusPublished - 6 Apr 2002
Externally publishedYes


  • Computer learning
  • Computer prediction
  • Kolmogorov complexity
  • Snooping


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