This section introduces several techniques used to learn from stream data when the true label for each instance is available. In particular, we present linear, non-linear, and ensemble-based algorithms adapted to incremental learning, as well as methods required in the evaluation and validation of these models, keeping in mind that learning is constrained by limits on memory and CPU time.
The modeling techniques are divided into linear algorithms, non-linear algorithms, and ensemble methods.
The linear methods described here require little to no adaptation to handle stream data.
Different loss functions such as hinge, logistic, and squared ...