We do this by fitting a replacement to m * : m ≤ m * , based on the classifier's training data, such that too-large elements will be accommodated into the stream by implicitly assuming that the portion of a value that falls outside the restricted window is "spread out" over the previously observed values.
we modify the contents of the implicit reservoir by rewriting history: pretending that earlier elements were larger than they were, but still within the reduced window.