摘要: In this chapter, the different methods and techniques used to learn from data streams in evolving nonstationary environments will be presented, their performances compared according generated drift characteristics as well application context objectives. The goal is define criteria order help readers efficiently design suitable learning scheme for a particular application. For aim, these are classified set of meaningful criteria. Several examples illustrate discuss principal performance techniques.
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