Decision boundary

A decision boundary is the region of a problem space in which the output label of a classifier is ambiguous.

This effect is common in fuzzy logic based classification algorithms, where membership in one class or another is ambiguous.

[4] Decision boundary instability can be incorporated with generalization error as a standard for selecting the most accurate and stable classifier.

If it has one hidden layer, then it can learn any continuous function on compact subsets of Rn as shown by the universal approximation theorem, thus it can have an arbitrary decision boundary.

In particular, support vector machines find a hyperplane that separates the feature space into two classes with the maximum margin.