Connectionist incremental learning by analogy

Abstract

The Connectionist Analogy Processor (CAP) is a neural network. The paradigm of CAP assumes relational isomorphism for analogical inference. An internal abstraction model is formed by backpropagation training with the aid of a pruning mechanism. CAP also automatically develops abstraction and de-abstraction mappings to link the general and specific entities. CAP is applied to incremental analogical learning that involves multiple sets of analogy. It is shown that a new set of target data are selectively bound to the right one of internal abstraction models acquired from the previous analogical learning, i.e., the abstraction model acts as the attractor in the weight parameter space.