No Free Lunch versus Occam's Razor in Supervised Learning — mentioned by antidesitter

Tue, 20 Nov 2018 18:48:18 GMT

The No Free Lunch Theorem is irrelevant to the real world [0][1]. It assumes all functions, even those with infinite algorithmic complexity, are equally likely. You should look into algorithmic probability for a better foundation. [0] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.540.... [1] https://arxiv.org/abs/1111.3846.pdf

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No Free Lunch versus Occam's Razor in Supervised Learning — mentioned by rsaarelm

Sat, 11 Jul 2015 05:25:26 GMT

Solomonoff induction seems to be an useful innate bias for the current approaches at general intelligence. It's basically a formalization of Occam's razor that states that instead of being completely random, the surrounding environment is mostly lawful and simple, and therefore most of the processes in it can be described by algorithms with short rather than long source code. Hutter has written a bit about NFL: http://arxiv.org/pdf/1111.3846.pdf http://arxiv.org/pdf/1105.5721.pdf

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No Free Lunch versus Occam's Razor in Supervised Learning · HackerPapers discussions

No Free Lunch versus Occam's Razor in Supervised Learning — mentioned by antidesitter

No Free Lunch versus Occam's Razor in Supervised Learning — mentioned by rsaarelm