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GS is a network for supervised inductive learning from examples that uses ideas from neural networks and symbolic inductive learning to gain benefits of both methods. The network is built of many simple nodes that learn important features in the input space and then monitor the ability of the features to predict output values. The network avoids the exponential nature of the number of features by using information gained by general features to guide the creation of more specific features. Empirical evaluation of the model on real world data has shown that the network provides good generalization performance. Convergence is accomplished within a small number of training passes. The network provides these benefits while automatically allocating and deleting nodes and without requiring user adjustment of any parameters. The network learns incrementally and operates in a parallel fashion.