There are nonlinear bounds for the perceptron that was voted on. The area of a problem space where a classifier's output label is ambiguous is known as a decision boundary.
The classification issue is linear and the classes are separable linearly if the decision surface is a hyperplane. Decision lines aren't always obvious. A linear model is not a decision stump. Even if the model is not linear, the decision boundary can still be a line. Consider logistic regression. It was initially proposed at a time when academics had a firm belief that mathematical models could be used to resolve issues with production planning for a period of time like a year.
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