Neural networks that teach themselves through genetic discovery of novel examples

Abstract

The authors introduce an active learning paradigm for neural networks. In contrast to the passive paradigm, the learning in the active paradigm is initiated by the machine learner instead of its environment or teacher. The authors present a learning algorithm that uses a genetic algorithm for creating novel examples to teach multilayer feedforward networks. The creative learning networks, based on their own knowledge, discover new examples, criticize and select useful ones, train themselves, and thereby extend their existing knowledge. Experiments on function extrapolation show that the self-teaching neural networks not only reduce the teaching efforts of the human, but the genetically created examples also contribute robustly to the improvement of generalization performance and the interpretation of the connectionist knowledge.<>