Online machine learning

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order . Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset . Online learning algorithms may be prone to catastrophic interference, a problem that can be addressed by incremental learning approaches . In statistical learning models, the training sample ( x i , y i ) displaystyle (x_i,y_i) are assumed to have been drawn from the true distribution p ( x , y ) displaystyle p(x,y) and the objective is to minimize the expected "risk"