No Free Lunch versus Occam's Razor in Supervised Learning

  title={No Free Lunch versus Occam's Razor in Supervised Learning},
  author={Tor Lattimore and Marcus Hutter},
  booktitle={Algorithmic Probability and Friends},
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… CONTINUE READING
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