Starting Small - Learning with Adaptive Sample Sizes

Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann

Abstract: For many machine learning problems, data is abundant and it may be prohibitive to make multiple passes through the full training set. In this context, we investigate strategies for dynamically incr...