Efficient sampling strategies for relational database operations
ICDT Selected papers of the 4th international conference on Database theory
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Artificial Intelligence
Analysis and application of adaptive sampling
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A sequential sampling algorithm for a general class of utility criteria
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Near-Optimal Reinforcement Learning in Polynominal Time
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
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DS '08 Proceedings of the 11th International Conference on Discovery Science
A new method for adaptive sequential sampling for learning and parameter estimation
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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A sequential sampling algorithm or adaptive sampling algorithm is a sampling algorithm that obtains instances sequentially one by one and determines from these instances whether it has already seen enough number of instances for achieving a given task. In this paper, we present two typical sequential sampling algorithms. By using simple estimation problems for our example, we explain when and how to use such sampling algorithms for designing adaptive learning algorithms.