Machine learning of inductive bias
Machine learning of inductive bias
Information Processing Letters
On generating all maximal independent sets
Information Processing Letters
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Computational limitations on learning from examples
Journal of the ACM (JACM)
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Machine Learning
On the Learnability of Disjunctive Normal Form Formulas
Machine Learning
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
On the complexity of dualization of monotone disjunctive normal forms
Journal of Algorithms
Complexity theoretic hardness results for query learning
Computational Complexity
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Machine Learning
A Version Space Approach to Learning Context-free Grammars
Machine Learning
Theoretical underpinnings of version spaces
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Generating all maximal independent sets of bounded-degree hypergraphs
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Machine Learning on the Basis of Formal Concept Analysis
Automation and Remote Control
AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Concept Learning with Approximation: Rough Version Spaces
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Version Space Learning with DNA Molecules
DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
Programming by Demonstration Using Version Space Algebra
Machine Learning
A Unifying Version-Space Representation
Annals of Mathematics and Artificial Intelligence
Learning approximate preconditions for methods in hierarchical plans
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning to identify student preconceptions from text
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
A SAT-based version space algorithm for acquiring constraint satisfaction problems
ECML'05 Proceedings of the 16th European conference on Machine Learning
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This paper shows that it is not necessary to maintain boundary sets to reason using version spaces. Rather, most of the operations typically performed on version spaces for a concept class can be tractably executed directly on the training data, as long as it is tractable to solve the consistency problem for that concept class -- to determine whether there exists any concept in the concept class that correctly classifies the data. The equivalence of version-space learning to the consistency problem bridges a gap between empirical and theoretical approaches to machine learning, since the consistency problem is already known to be critical to learning in the PAC (Probably Approximately Correct) sense. By exhibiting this link to the consistency problem, we broaden the class of problems to which version spaces can be applied to include concept classes where boundary sets can have exponential or infinite size and cases where boundary sets are not even well defined.