VISUALIZING LEARNING AND COMPUTATION IN ARTIFICIAL NEURAL NETWORKS
Mark W. Craven, JUDE W. SHAVLIK · International Journal of Artificial Intelligence Tools · 1992
Scientific visualization is the process of using graphical images to form succinct and lucid representations of numerical data. Visualization has proven to be a useful method for understanding both learning and computation in artificial neural networks. While providing a powerful and general technique for inductive learning, artificial neural networks are difficult to comprehend because they form representations that are encoded by a large number of real-valued parameters. By viewing these parameters pictorially, a better understanding can be gained of how a network maps inputs into outputs. In this article, we survey a number of visualization techniques for understanding the learning and decision-making processes of neural networks. We also describe our work in knowledge-based neural networks and the visualization techniques we have used to understand these networks. In a knowledge-based neural network, the topology and initial weight values of the network are determined by an approximately-correct set of inference rules. Knowledge-based networks are easier to interpret than conventional networks because of the synergy between visualization methods and the relation of the networks to symbolic rules.