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The No-Free-Lunch Theorems of Supervised Learning

Sterkenburg, Tom F. and Grünwald, Peter D. (2021) The No-Free-Lunch Theorems of Supervised Learning. Synthese.

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Abstract

The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as purely data-driven. On this conception, every algorithm must have an inherent inductive bias, that wants justification. We argue that many standard learning algorithms should rather be understood as model-dependent: in each application they also require for input a model, representing a bias. Generic algorithms themselves, they can be given a model-relative justification.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Sterkenburg, Tom F.tom.sterkenburg@lmu.de0000-0002-4860-727X
Grünwald, Peter D.
Subjects: Specific Sciences > Computer Science
General Issues > Confirmation/Induction
Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User: Mr Tom Sterkenburg
Date Deposited: 14 Jul 2021 02:25
Last Modified: 14 Jul 2021 02:25
Item ID: 19300
Journal or Publication Title: Synthese
DOI or Unique Handle: 10.1007/s11229-021-03233-1
Subjects: Specific Sciences > Computer Science
General Issues > Confirmation/Induction
Specific Sciences > Artificial Intelligence > Machine Learning
Date: 2021
URI: http://philsci-archive.pitt.edu/id/eprint/19300

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