| >> Erm, no. Not unless they are solving the problem perfectly. Well, yes, that's what I mean. I gave an example here a while ago, of how a Meta-Interpretive Learning
algorithm, Metagol, can learn the aⁿbⁿ grammar perfectly from 4 positive
examples: https://news.ycombinator.com/item?id=17837055 That's typical of Metagol, as well as other algorithms in Inductive Logic Programming, the broader sub-field of machine learning that MIL belongs to. >> Do feel free to share an example of an algorithm that generalizes better from
less data. I'll wait. To clarify, my claim is that there are algorithms that learn adquately from
few data and therefore don't "need" more data. Not that less data is better. That said, there are theoretical results that suggest that a larger hypothesis
space increases the chance of the learner overfitting to noise. So what is
really needed in order to improve generalisation is not more data, but more
relevant data. Then again, that is the subject of my current PhD so I might
just be interpreting everything through the lens of my research (as is typical for PhD students). You work in the field? What do you do? |