Communications of the ACM
The Strength of Weak Learnability
Machine Learning
Efficient sampling strategies for relational database operations
ICDT Selected papers of the 4th international conference on Database theory
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
Query size estimation by adaptive sampling
Selected papers of the 9th annual ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Boosting a weak learning algorithm by majority
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Annals of Mathematics and Artificial Intelligence
Practical Algorithms for On-line Sampling
DS '98 Proceedings of the First International Conference on Discovery Science
A complete and tight average-case analysis of learning monomials
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
Scaling Up a Boosting-Based Learner via Adaptive Sampling
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E3 Algorithm
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
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Machine learning has been one of the important subjects of AI that is motivated by many real world applications. In theoretical computer science, researchers also have introduced mathematical frameworks for investigating machine learning, and in these frameworks, many interesting results have been obtained. Now we are proceeding to a new stage to study how to apply these fruitful theoretical results to real problems. We point out in this paper that "adaptivity" is one of the important issues when we consider applications of learning techniques, and we propose one learning algorithm with this feature.