Support Vector Machines aim to maximise the margin between classes

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BlackTom AI · Accepted Answer

The question asks whether Support Vector Machines aim to maximise the margin between classes.
Option 1: True. A core idea in SVM theory is to find the decision boundary (hyperplane) that separates classes with the largest possible margin. In the hard-margin case, this margin is the distance between the hyperplane and the nearest points from each class, which are the support vectors. In the soft-margin variant, the objective still revolves around maximizing that margin while allowing some misclassifications via slack variables and the regularization parameter C. Conceptually, this is equivalent to minimizing the norm of the weight vector, which leads to a larger margin when feasible.
Option 2: False. If one argued that SVMs do not maximize margin, this would contradict the foundational formulation of SVMs where the margin is explicitly the quantity being maximized (or, equivalently, the weight norm is minimized under margin constraints). A common misconception might be to equate SVMs with simply fitting the data or maximizing accuracy, but the mathematical objective is about margin, not just misclassification reduction. In the soft-margin variant, while some misclassifications are allowed, the optimization still prioritizes a larger margin subject to those slack constraints, not a naive accuracy maximization.
Option 1 (true) remains aligned with the theoretical goal of SVMs across both hard and soft margin formulations, whereas Option 2 (false) relies on a misinterpretation that SVMs ignore margin or prioritize something else entirely. The distinction hinges on whether one recognizes margin maximization as the central objective, and in standard SVM theory, it is indeed the guiding principle.

Support Vector Machines aim to maximise the margin between classes

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