Learning k-DNF with noise in the attributes
COLT '88 Proceedings of the first annual workshop on Computational learning theory
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Solving Computational Learning Problems of Boolean Formulae on DNA Computers
DNA '00 Revised Papers from the 6th International Workshop on DNA-Based Computers: DNA Computing
Experimental Construction of Very Large Scale DNA Databases with Associative Search Capability
DNA 7 Revised Papers from the 7th International Workshop on DNA-Based Computers: DNA Computing
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
A bayesian algorithm for in vitro molecular evolution of pattern classifiers
DNA'04 Proceedings of the 10th international conference on DNA computing
| Hi-index | 0.00 |
We introduce a class of generalized DNF formulae called wDNF or weighted disjunctive normal form, and present a molecular algorithm that learns a wDNF formula from training examples. Realized in DNA molecules, the wDNF machines have a natural probabilistic semantics, allowing for their application beyond the pure Boolean logical structure of the standard DNF to real-life problems with uncertainty. The potential of the molecular wDNF machines is evaluated on real-life genomics data in simulation. Our empirical results suggest the possibility of building error-resilient molecular computers that are able to learn from data, potentially from wet DNA data.