 It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g., stock price prediction.Online learning algorithms may be prone to catastrophic interference, a problem that can be addressed by incremental learning approaches.In the setting of supervised learning, a function of formula_1 is to be learned, where formula_2 is thought of as a space of inputs and formula_3 as a space of outputs, that predicts well on instances that are drawn from a joint probability distribution formula_4 on formula_5.