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The No-Free-Lunch Theorems of Supervised Learning

Sterkenburg, Tom F. and Grünwald, Peter D. (2020) The No-Free-Lunch Theorems of Supervised Learning. [Preprint]

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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: Preprint
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: 11 Dec 2020 14:43
Last Modified: 11 Dec 2020 14:43
Item ID: 18505
Subjects: Specific Sciences > Computer Science
General Issues > Confirmation/Induction
Specific Sciences > Artificial Intelligence > Machine Learning
Date: 2020
URI: http://philsci-archive.pitt.edu/id/eprint/18505

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