
Chapter 3
Change Detection
Most of the machine learning algorithms assume that examples are generated
at random, according to some stationary probability distribution. In this chap-
ter, we study the problem of learning when the distribution that generates the
examples changes over time. Embedding change detection in the learning pro-
cess is one of the most challenging problems when learning from data streams.
We review the Machine Learning literature for learning in the presence of drift,
discuss the major issues to detect and adapt decision problems when learning
from streams with unknown dynamics, and present illustrative algorithms to
detect changes ...