Learning internal representations

We introduce a learning algorithm for multilayer neural networks composed of binary linear threshold elements. Whereas existing algorithms reduce the learning process to minimizing a cost function over the weights, our method treats the internal ...

This paper describes an integrative machine learning architecture of imitation learning and reinforcement learning. The learning architecture aims to help integration of the two learning process by generating internal rewards. After observing superiors, ...

Animal learning is associated with changes in the efficacy of connections between neurons. The rules that govern this plasticity can be tested in neural networks. Rules that train neural networks to map stimuli onto outputs are given by supervised ...