No Free Lunch versus Occam’s Razor in Supervised Learning

  • Tor Lattimore
  • Marcus Hutter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7070)

Abstract

The No Free Lunch theorems are often used to argue that domain specific knowledge is required to design successful algorithms. We use algorithmic information theory to argue the case for a universal bias allowing an algorithm to succeed in all interesting problem domains. Additionally, we give a new algorithm for off-line classification, inspired by Solomonoff induction, with good performance on all structured (compressible) problems under reasonable assumptions. This includes a proof of the efficacy of the well-known heuristic of randomly selecting training data in the hope of reducing the misclassification rate.

Keywords

Supervised learning Kolmogorov complexity no free lunch Occam’s razor 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tor Lattimore
    • 1
  • Marcus Hutter
    • 1
    • 2
    • 3
  1. 1.Australian National UniversityCanberraAustralia
  2. 2.ETH ZürichSwitzerland
  3. 3.NICTAAustralia

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