Properties of a New Adaptive Sampling Method with Applications to Scalable Learning

Sampling is an important technique for parameter estimation and hypothesis testing widely used in statistical analysis, machine learning and knowledge discovery. Adaptive sampling offers advantages over traditional batch sampling methods in that adaptive sampling often uses much lower number of samples and thus better efficiency while assuring guaranteed level of estimation accuracy and confidence. In our previous works, a new adaptive sampling method was developed, and applied to build an efficient, scalable boosting learning algorithm. In this paper, we present a preliminary theoretical analysis of the proposed sampling method. A new variant of the sampling method is also presented. Empirical simulation results indicate that our methods, both the new variant and the original algorithm, often use significantly lower sample size (i.e., the number of sampled instances) while maintaining competitive accuracy and confidence when compared with batch sampling method.