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 | |||||||||
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| Subjects: | Specific Sciences > Computer Science General Issues > Confirmation/Induction Specific Sciences > Artificial Intelligence > Machine Learning |
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| 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 |
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| Date: | 2020 | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/18505 |
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