From batch to online learning using copies

In many every day examples trained machine learning models are rendered obsolete by an inability to adapt to an ever changing environment. This may happen either because their performance decreases in time or because external agents impose new constraints, for example, in the form of regulations. This situation is particularly worrying in company productions environments where model accuracy needs to be preserved. In such situations, model-agnostic copies have been proposed as a viable method to adapt pre-existing models to the new requirements. In this article we study how the use of copies can be extended to endow classifiers trained in batch with online learning. We propose two online algorithms and validate their performance in a series of well-known problems.