An example is the graph convolutional kernel support vector machine (GCKSVM) for node classification, for which we analyze the generalization error bound and discuss the impact of the graph structure.

Quantitatively, we show a variety of bounds relating the number of mistakes to well-known combinatorial dimensions.

Forsuch models, one particularly elegant approach is that ofpassive-aggressive learning[3]. In this framework, a model isonly updated when itfails to classify an example correctly with high confidence.