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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
68 Knowledge Discovery from Data Streams
bound (Chernoff, 1952):
ε
c
=
r
3 × ¯µ
n
ln(2),
where δ is a user-defined confidence level. In the case of bounded loss functions,
like the 0-1 loss, the Hoeffding bound (Hoeffding, 1963) can be used:
ε
h
=
s
R
2n
ln
2
δ
,
where R is the range of the random variable. Both bounds use the sum of inde-
pendent random variables and give a relative or absolute approximation of the
deviation of X from its expectation. They are independent of the distribution
of the random variable.
5.3.2.1 Error Estimators Using a Single Algorithm and a Single
Dataset
Prequential evaluation provides a learning curve that monitors the evo-
lution of learning ...
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Publisher Resources

ISBN: 9781439826126