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Efficiently Determining the Starting Sample Size for Progressive Sampling
EMCL '01: Proceedings of the 12th European Conference on Machine LearningGiven a large data set and a classification learning algorithm, Progressive Sampling (PS) uses increasingly larger random samples to learn until model accuracy no longer improves. It is shown that the technique is remarkably efficient compared to using ...
Reduction of sample sizes in network sampling models
This paper develops and analyzes some statistical sampling methods suitable in a class of telecommunications applications including network cost calculations. The main objective is to obtain the smallest possible sample from a population in such a way ...
Efficient sample reuse in policy search by multiple importance sampling
GECCO '18: Proceedings of the Genetic and Evolutionary Computation ConferencePolicy search such as reinforcement learning and evolutionary computation is a framework for finding an optimal policy of control problems, but it usually requires a huge number of samples. Importance sampling is a common tool to use samples drawn from ...






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