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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
158 Knowledge Discovery from Data Streams
Algorithm 25: The Online Bagging Algorithm.
input: L
o
: online base Learning Algorithm
h: Set of learned models {h
1
, . . . , h
m
}
(x,y): Latest training example to arrive
begin
foreach base model h
m
h do
Set k according to Poisson(1)
for i = 1 to k do
h
m
L
o
(h
m
, (x, y))
end
method able to avoid this requirement. Each original training example may be
replicated zero, one, or more times in each bootstrap training set because the
sampling is done with replacement. Each base model is trained with k copies
of each of the original training examples where:
P (k) =
exp(1)
k!
(10.1)
As each training example is available, and
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Publisher Resources

ISBN: 9781439826126