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  1. The no-free-lunch theorems of supervised learning.Tom F. Sterkenburg & Peter D. Grünwald - forthcoming - Synthese:1-37.
    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 (...)
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  • Introduction.Elay Shech & Wendy S. Parker - 2021 - Studies in History and Philosophy of Science Part A 85:30-33.
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