Support Vector Machines aim to maximise the margin between classes

Question

BlackTom AI · Accepted Answer

Question: Support Vector Machines aim to maximise the margin between classes

Option 1: True
- Explanation: In a classic (hard-margin) SVM, we seek a hyperplane that separates the classes with the largest possible margin, where the margin is defined as the distance from the hyperplane to the nearest data points (the support vectors) from either class. Maximizing this margin is central to the SVM objective because a larger margin is associated with better generalization to unseen data.

Option 2: False
- Explanation: This statement contradicts the fundamental purpose of SVMs. The core idea of an SVM is precisely to find the decision boundary that maximizes the margin between the closest points of the two classes. Saying that SVMs do not aim to maximize the margin would ignore the central optimization criterion of the method.

Additional context (optional): When data are not perfectly linearly separable, soft-margin SVMs introduce slack variables to allow some misclassifications while still maximizing the margin as much as possible. Despite this relaxation, the underlying objective remains to maximize margin (or minimize a margin-based objective) to improve generalization, rather than merely fitting the training data.

Support Vector Machines aim to maximise the margin between classes

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