This is related to a theorem that I have proved and its relation (or not) to an existing result.
Essentially, I have shown that PAC-learning is undecidable in the Turing sense. The arxiv link to the paper is https://arxiv.org/abs/1808.06324
I am told that this is provable as a corollary of existing results. I was hinted that the fundamental theorem of statistical machine learning that relates the VC dimension and PAC-learning could be used to prove the undecidability of PAC-learning. An arxiv link to a set of notes for this is https://arxiv.org/abs/1507.05307
I am confused, however. I understand that that the above theorem stipulates a condition for the learnability of functions and does not comment about the Turing-decidability of the same.
Can someone help me, please?
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