Given the task definition and input, reply with output. In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Abstract. We study the learning algorithm corresponding to the incremental gradient descent defined by the empirical risk over an infinite dimensional hypotheses space. We consider a statistical learning setting and show that, provided with a universal step-size and a suitable early stopping rule, the learning algorithm thus obtained is universally consistent and derive finite sample bounds. Our results provide a theoretical foundation for considering early stopping in online learning algorithms and shed light on the effect of allowing for multiple passes over the data.
REGULARIZATION BY EARLY STOPPING FOR ONLINE LEARNING ALGORITHMS