On the learnability of Boolean formulae
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Computational limitations on learning from examples
Journal of the ACM (JACM)
Toward efficient agnostic learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning in the presence of malicious errors
SIAM Journal on Computing
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Robust trainability of single neurons
Journal of Computer and System Sciences
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
A threshold of ln n for approximating set cover (preliminary version)
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
The hardness of approximate optima in lattices, codes, and systems of linear equations
Journal of Computer and System Sciences - Special issue: papers from the 32nd and 34th annual symposia on foundations of computer science, Oct. 2–4, 1991 and Nov. 3–5, 1993
Some optimal inapproximability results
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Machine Learning
Hardness Results for Neural Network Approximation Problems
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Maximizing Agreements and CoAgnostic Learning
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
On the Difficulty of Approximately Maximizing Agreements
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Hardness Results for General Two-Layer Neural Networks
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Approximate graph coloring by semidefinite programming
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Maximizing Agreements and CoAgnostic Learning
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Maximizing agreements and coagnostic learning
Theoretical Computer Science - Algorithmic learning theory(ALT 2002)
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Many studies have been done in the literature on minimum disagreement problems and their connection to Agnostic learning and learning with Malicious errors. We further study these problems and some extensions of them. The classes that are studied in the literature are monomials, monotone monomials, antimonotone monomials, decision lists, halfspaces, neural networks and balls.F or some of these classes we improve on the best previously known factors for approximating the minimum disagreement. We also find new bounds for exclusive-or, k-term DNF, k-DNF and multivariate polynomials (Xor of monomials).We then apply the above and some other results from the literature to Agnostic learning and give negative and positive results for Agnostic learning and PAC learning with malicious errors of the above classes.