Communications of the ACM
Learning in the presence of malicious errors
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Computational limitations on learning from examples
Journal of the ACM (JACM)
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
On the necessity of Occam algorithms
Theoretical Computer Science
An introduction to computational learning theory
An introduction to computational learning theory
Population Computation and Majority Inference in Test Tube
DNA 7 Revised Papers from the 7th International Workshop on DNA-Based Computers: DNA Computing
Version Space Learning with DNA Molecules
DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
Molecular learning of wDNF formulae
DNA'05 Proceedings of the 11th international conference on DNA Computing
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We apply a DNA-based massively parallel exhaustive search to solving the computational learning problems of DNF (disjunctive normal form) Boolean formulae. Learning DNF formulae from examples is one of the most important open problems in computational learning theory and the problem of learning 3-term DNF formulae is known as intractable if RP ≠ NP. We propose new methods to encode any k-term DNF formula to a DNA strand, evaluate the encoded DNF formula for a truth-value assignment by using hybridization and PCR, and find a consistent DNF formula with the given examples. By employing these methods, we show that the class of k-term DNF formulae (for any constant k) and the class of general DNF formulae are efficiently learnable on DNA computer.