Feed-forward neural networks based on self-extracted knowledge

We introduce a hybrid model of neural network learning and the learned knowledge. It involves neural network training with domain data, transforming connection weights of the trained network into symbolic rules; and building a new network with the symbolic knowledge. In this article, we show that knowledge can be represented in either of the following two different forms: connection weights or symbolic rules, which are mutually interchangeable. The hybrid model provides better structure complexity and better performance over other models with neural network only or symbolic rule base only. Empirical results are also shown.