Communications of the ACM
Theory of linear and integer programming
Theory of linear and integer programming
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
Learning Nested Differences of Intersection-Closed Concept Classes
Machine Learning
Machine learning: a theoretical approach
Machine learning: a theoretical approach
SIAM Journal on Computing
On learning ring-sum-expansions
SIAM Journal on Computing
Learning in the presence of malicious errors
SIAM Journal on Computing
On-line learning of rectangles in noisy environments
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
A result of Vapnik with applications
Discrete Applied Mathematics
On learning counting functions with queries
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
The weighted majority algorithm
Information and Computation
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Predicting {0, 1}-functions on randomly drawn points
Information and Computation
Simulating access to hidden information while learning
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
On-line prediction and conversion strategies
Euro-COLT '93 Proceedings of the first European conference on Computational learning theory
Machine Learning
Machine Learning
Learning Monotone DNF from a Teacher That Almost Does Not Answer Membership Queries
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Smooth Boosting and Learning with Malicious Noise
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Learning monotone dnf from a teacher that almost does not answer membership queries
The Journal of Machine Learning Research
Smooth boosting and learning with malicious noise
The Journal of Machine Learning Research
A new PAC bound for intersection-closed concept classes
Machine Learning
Learning intersection-closed classes with signatures
Theoretical Computer Science
Machine learning in adversarial environments
Machine Learning
Purifying data by machine learning with certainty levels
Proceedings of the Third International Workshop on Reliability, Availability, and Security
Learning attribute-efficiently with corrupt oracles
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
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We investigate a variant of the on‐line learning model for classes of \{0,1\}‐valued functions (concepts) in which the labels of a certain amount of the input instances are corrupted by adversarial noise. We propose an extension of a general learning strategy, known as “Closure Algorithm”, to this noise model, and show a worst‐case mistake bound of m + (d+1)K for learning an arbitrary intersection‐closed concept class \mathcal{C}, where K is the number of noisy labels, d is a combinatorial parameter measuring \mathcal{C}’s complexity, and m is the worst‐case mistake bound of the Closure Algorithm for learning \mathcal{C} in the noise‐free model. For several concept classes our extended Closure Algorithm is efficient and can tolerate a noise rate up to the information‐theoretic upper bound. Finally, we show how to efficiently turn any algorithm for the on‐line noise model into a learning algorithm for the PAC model with malicious noise.