Difference between revisions of "Online machine learning"
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* The number of examples (rows) are very large and they are assumed to keep arriving in a stream infinitely. | * 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. | * 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. | ||
| + | * Adaptation approach: custom built for stream learning. | ||
Revision as of 14:10, 24 March 2012
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.
- Adaptation approach: custom built for stream learning.
