The batch machine learning methods that we have applied in this book focus on processing an existing fixed set of training data. Typically, these techniques are also iterative, and we have performed multiple passes over our training data in order to converge to an optimal model.
By contrast, online learning is based on performing only one sequential pass through the training data in a fully incremental fashion (that is, one training example at a time). After seeing each training example, the model makes a prediction for this example and then receives the true outcome (for example, the label for classification or real target for regression). The idea behind online learning is that the model continually updates as new information is received, instead of being retrained periodically in batch training.
In some settings, when the data volume is very large or the process that generates the data is changing...