Seer: Maximum likelihood regression for learning-speed curves

The research presented here focuses on modeling machine-learning performance. The thesis introduces Seer, a system that generates empirical observations of classification-learning performance and then uses those observations to create statistical models. The models can be used to predict the number of training examples needed to achieve a desired level and the maximum accuracy possible given an unlimited number of training examples. Seer advances the state of the art with (1) models that embody the best constraints for classification learning and most useful parameters, (2) algorithms that efficiently find maximum-likelihood models, and (3) a demonstration on real-world data from three domains of a practicable application of such modeling.