An approach for generalizing knowledge based on rules with priority orders

Based on some similarities between the knowledge system composed of the rules with priority orders and the sequential learning ahead masking (SLAM) model, an approach to enhance the generalization capabilities of the former with the help of the later is advocated. Firstly, the mapping from a rule to weights is realized. Secondly, the SLAM model is initialized to contain the knowledge from the rules and the generalization capabilities of the model are improved through the adjustment of the weights. Thirdly, based on the model, the approach can realize the incremental learning to grasp the knowledge containing in the newly added instances. Finally, the experimentations testify the obtained model has stronger generalization capabilities.