Online Learning
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Online learning involves using the data available and updating the model directly before a prediction is required or after the last observation was made. Online learning is appropriate for those problems where observations are provided over time and where the probability distribution of observations is expected to also change over time. Therefore, the model is expected to change just as frequently in order to capture and harness those changes. This approach is also used by algorithms where there may be more observations than can reasonably fit into memory, therefore, learning is performed incrementally over observations, such as a stream of data. Generally, online learning seeks to minimize “regret,” which is how well the model performed compared to how well it might have performed if all the available information was available as a batch. One example of online learning is so-called stochastic or online gradient descent used to fit an artificial neural network. 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery
