Communications of the ACM
Computational limitations on learning from examples
Journal of the ACM (JACM)
A general lower bound on the number of examples needed for learning
Information and Computation
Crytographic limitations on learning Boolean formulae and finite automata
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
The computational complexity of machine learning
The computational complexity of machine learning
Equivalence of models for polynomial learnability
Information and Computation
Computational learning theory: an introduction
Computational learning theory: an introduction
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
An introduction to computational learning theory
An introduction to computational learning theory
Robust trainability of single neurons
Journal of Computer and System Sciences
The nature of statistical learning theory
The nature of statistical learning theory
General bounds on the number of examples needed for learning probabilistic concepts
Journal of Computer and System Sciences
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
On Learning Sets and Functions
Machine Learning
Maximizing Agreements and CoAgnostic Learning
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
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This article is a brief exposition of some of the important links between machine learning and combinatorial optimization. We explain how efficient 'learnability' in standard probabilistic models of learning is linked to the existence of efficient randomized algorithms for certain natural combinatorial optimization problems, and we discuss the complexity of some of these optimization problems.