An optimal online algorithm for metrical task systems
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Learning in the presence of malicious errors
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Competitive algorithms for server problems
Journal of Algorithms
Types of noise in data for concept learning
COLT '88 Proceedings of the first annual workshop on Computational learning theory
On the power of randomization in online algorithms
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
Tracking drifting concepts using random examples
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Modeling time varying system using hidden control neural architecture
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Toward efficient agnostic learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Machine Learning
Learning with restricted focus of attention
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Learning to model sequences generated by switching distributions
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Learning changing concepts by exploiting the structure of change
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
The complexity of learning according to two models of a drifting environment
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
On Restricted-Focus-of-Attention Learnability of Boolean Functions
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
The Complexity of Learning According to Two Models of a Drifting Environment
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Predictive learning models for concept drift
Theoretical Computer Science - Algorithmic learning theory
Learning Changing Concepts by Exploiting the Structure of Change
Machine Learning
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Predictive Learning Models for Concept Drift
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Partial observability and learnability
Artificial Intelligence
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We consider learning in situations where the function used to classify examples may switch back and forth between a small number of different concepts during the course of learning. We examine several models for such situations: oblivious models in which switches are made independent of the selection of examples, and more adversarial models in which a single adversary controls both the concept switches and example selections.We show relationships between the more benign models and the p-concepts of Kearns and Schapire, and present polynomial-time algorithms for learning switches between two k-DNF formulas. For the most adversarial model, we present a model of success patterned after the popular competitive analysis used in studying on-line algorithms. We describe a randomized query algorithm for such adversarial switches between two monotone disjunctions that is “1-competitive” in that the total number of mistakes plus queries is with high probability bounded by the number of switches plus some fixed polynomial in n (the number of variables).We also use notions described here to provide sufficient conditions under which learning a p-concept class “with a decision rule” implies being able to learn the class “with a model of probability.”.