View source for Online machine learning

= Stream mining = 

* Conventional machine learning assumes that the training data is available as a whole set.
* In stream mining, core assumption is that '''training''' examples can be briefly inspected for a single time only.
* Arrive in a high speed stream and then must be discarded to make room for subsequent examples.
* Algorithm must update its model incrementally as each element is inspected.
* It should ideally be able to apply the model any time between training examples.
* the goal of classification is to produce a model that can predict the class of unlabelled examples by training on examples whose label/class is supplied.
* Data is assumed to have small and fixed no. of columns or attributes/features. This can be thought of as tuples.
* The number of examples (rows) are very large and they are assumed to keep arriving in a stream infinitely.
* The labels that can be applied to the data are limited, typically less than ten.

== Approaches ==

* Wrapper approach : collect a stream into a batch and then use batch ML. Not very good.
* Adaptation approach: custom built for stream learning.
** Decision Tree: Good choice.
** Neural Networks: Also good.