While SVM is used most often in classification scenarios, it can be extended to regression cases by allowing the user to provide a maximum margin of error allowed for any observation.
Common choices for kernels include: Linear, Polynomial, Radial Basis, Sigmoid
While soft margin classification relaxes the requirement of a hyperplane that must perfectly distinguish between the classes, a separate issue arises when there is no way to define such a hyperplane in the original feature space.
While the maximum margin classifier is optimal in theory, in practice, observations cannot be perfectly separated in most classification problems.
SVM is a classification algorithm that seeks to determine a decision boundary by maximizing the distance between points of different classes.
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