4 ms·
The no-free-lunch theorem is interesting in theory but not terribly relevant in practice. A prior that something can be efficiently approximated will learn anyt
by im3w1l 6y ago
The no-free-lunch theorem is interesting in theory but not terribly relevant in practice. A prior that something can be efficiently approximated will learn anything you could desire to learn, and even stronger assumptions will work for most real world problems.
- Retric 6y agoNFR is extremely important when you’re actually trying to solve specific problems. If the ideal solution involves some output randomness then neutral networks on their own are useless. Poker for example is a terrible fit.
- red75prime 6y agoIt applies to humans too. We are terrible at generating truly random numbers, which are required for mixed game strategies. BTW, NFL theorem proves that it itself is never terribly important. Learning algorithm, which uses NFL theorem for making decisions, are in the same position as any other learning algorithm.
- Retric 6y agoSort of, the universe isn’t completely random so we have metadata we can apply to relevant problems. It’s the same reason that in theory compression algorithms fail, but in practice that’s not an issue. Though for arbitrary theoretical problems it’s important.