Communications of the ACM
Some results and experiments in programming techniques for propositional logic
Computers and Operations Research - Special issue: Applications of integer programming
A quantitative approach to logical inference
Decision Support Systems
Computation-oriented reductions of predicate to propositional logic
Decision Support Systems
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Computational limitations on learning from examples
Journal of the ACM (JACM)
Generalized resolution and cutting planes
Annals of Operations Research
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Modeling and integer programming techniques applied to propositional calculus
Computers and Operations Research - Special issue: Expert systems and operations research
Computational experience with an interior point algorithm on the satisfiability problem
Annals of Operations Research
Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
An interior point algorithm to solve computationally difficult set covering problems
Mathematical Programming: Series A and B - Special issue on interior point methods for linear programming: theory and practice
A continuous approach to inductive inference
Mathematical Programming: Series A and B
Generating logical expressions from positive and negative examples via a branch-and-bound approach
Computers and Operations Research
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Learning disjunction of conjunctions
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
A feature mining based approach for the classification of text documents into disjoint classes
Information Processing and Management: an International Journal
Computers and Operations Research
Computational Biology and Chemistry
Mathematical and Computer Modelling: An International Journal
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A critical aspect in the problem of inductive inference is the number of examples needed to accurately infer a Boolean function from positive and negative examples. In this paper, we develop an approach for deriving a sequence of examples for this problem. Some computer experiments indicate that, on the average, examples derived according to the proposed approach lead to the inference of the correct function considerably faster than when examples are derived in a random order.