The pattern match learning of binary neural networks
二进神经网络的模式匹配学习

It is necessary to know the logical meaning of every binary neuron when extracting knowledge from a binary neural network. Generally, it is difficult to analyze learning results of a learning algorithm for binary neural networks. In this paper, a new learning method is presented which is based on analyzing a set of linear separable structures. The most important benefit of this method is all binary neurons belong to one or more types of linear separable structure sets. If those linear separable structure sets have clear logical meaning, the whole knowledge of binary neural networks can be dug out.