A nearest hyperrectangle learning method

摘要

This paper presents a theory of learning called nested generalized exemplar (NGE) theory, in which learning is accomplished by storing objects in Euclidean n-space, En, as hyperrectangles. The hyperrectangles may be nested inside one another to arbitrary depth. In contrast to generalization processes that replace symbolic formulae by more general formulae, the NGE algorithm modifies hyperrectangles by growing and reshaping them in a well-defined fashion. The axes of these hyperrectangles are defined by the variables measured for each example. Each variable can have any range on the real line; thus the theory is not restricted to symbolic or binary values.