This is called instance-based learning: the system learns the examples by heart, then generalizes to new cases by using a similarity measure to compare them to the learned examples (or a subset of them). For example, in Figure 1-15 the new instance would be classified as a triangle because the majority of the most similar instances belong to that class.
