Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Artificial intelligence: theory and practice
Artificial intelligence: theory and practice
Machine Learning
Machine Learning
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
DNA Computing in Microreactors
DNA 7 Revised Papers from the 7th International Workshop on DNA-Based Computers: DNA Computing
Version spaces without boundary sets
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Molecular programming: evolving genetic programs in a test tube
GECCO '05 Proceedings of the 7th annual conference on Genetic and 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 |
Version space is used in inductive concept learning to represent the hypothesis space where the goal concept is expressed as a conjunction of attribute values. The size of the version space increases exponentially with the number of attributes. We present an efficient method for representing the version space with DNA molecules and demonstrate its effectiveness by experimental results. Primitive operations to maintain a version space are derived and their DNA implementations are described. We also propose a novel method for robust decision-making that exploits the huge number of DNA molecules representing the version space.