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Knowledge Discovery from Data Streams
book

Knowledge Discovery from Data Streams

by Joao Gama
May 2010
Intermediate to advanced content levelIntermediate to advanced
255 pages
8h 11m
English
Chapman and Hall/CRC
Content preview from Knowledge Discovery from Data Streams
38 Knowledge Discovery from Data Streams
Dynamic environments with non-stationary distributions require the for-
getfulness of the observations not consistent with the actual behavior of the
nature. Drift detection algorithms must not only adapt the decision model
to newer information but also forget old information. The memory model
also indicates the forgetting mechanism. Weighting examples corresponds to a
gradual forgetting. The relevance of old information is less and less important.
Time windows correspond to abrupt forgetting (weight equal 0). The exam-
ples are deleted from memory. We can combine, of course, both forgetting
mechanisms by weighting the examples in a time window (see Klinkenberg
(2004)).
3.2.2.2 Detection Methods
The Detection
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Publisher Resources

ISBN: 9781439826126