Classification by active testing with applications to imaging and change detection
In this dissertation, we investigate adaptive strategies for sequential testing, especially those driven by maximizing information gain when the conditional distribution of tests given hypotheses is Gaussian. We implement a classification algorithm in which tests are selected recursively and adaptively on-line. We show that such information-based strategies are statistically sensible and computationally efficient, and accommodate testing at multiple resolutions. Finally, applications are made to change point detection and medical image classification.