Knowledge acquisition and revision via neural networks
College of Computer Studies
IEEE International Conference on Neural Networks - Conference Proceedings
We investigate how knowledge acquired by a neural network from one input environment can be transferred and revised for similar application in a new environment. Knowledge revision is achieved by re-training the neural network. Knowledge common to both environments are retained, while localized knowledge components are introduced during network retraining. Various network performance measures are computed to measure how much knowledge is transferred and revised. Furthermore, because the knowledge acquired by a neural network can be expressed as an accurate set of simple rules, we are able to compare knowledge extracted from one network with that from another. In a cross-national study of car image perceptions, a comparison of the original and revised knowledge gives us insights into the commonalities and differences in brand perceptions across countries.
Digitial Object Identifier (DOI)
Azcarraga, A., Hsieh, M., Pan, S., & Setiono, R. (2004). Knowledge acquisition and revision via neural networks. IEEE International Conference on Neural Networks - Conference Proceedings, 2, 1365-1370. https://doi.org/10.1109/IJCNN.2004.1380147
Computer Sciences | Software Engineering
Neural networks (Computer science); Self-organizing systems