Instance-based learning

In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed, these algorithms are sometimes referred to as "lazy."