Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  show that randomly generated c log(n)-DNF formula can be learned exactly in probabilistic polynomial time using randomly generated examples. Our notion of randomly generated is with respect to a uniform distribution.   To prove this  extend the concept of  ll behaved  c log(n)-Monotone DNF formulae to c log(n)-DNF formulae, and show that almost every DNF formula is ll-behaved, and that there exists a probabilistic polynomial time algorithm that exactly learns all ll behaved c log(n)-DNF formula. This is the first algorithm that properly learns (non-monotone) DNF with a polynomial number of terms from random examples alone.
['Linda Sellie']
I am writing a research paper on a idea Exact learning of random DNF over the uniform distribution
[{"from": "human", "value": "I am writing a research paper on a idea Exact learning of random DNF over the uniform distribution"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  show that randomly generated c log(n)-DNF formula can be learned exactly in probabilistic polynomial time using randomly generated examples. Our notion of randomly generated is with respect to a uniform distribution.   To prove this  extend the concept of  ll behaved  c log(n)-Monotone DNF formulae to c log(n)-DNF formulae, and show that almost every DNF formula is ll-behaved, and that there exists a probabilistic polynomial time algorithm that exactly learns all ll behaved c log(n)-DNF formula. This is the first algorithm that properly learns (non-monotone) DNF with a polynomial number of terms from random examples alone."}]