Lecture
Support Vector Machines: SVMs
Description
This lecture covers the concepts of hard-margin and soft-margin Support Vector Machines (SVMs), including the geometric construction, calculation of the margin, optimization problems, support vectors, slack variables, and imbalanced classification. It also discusses the hinge loss interpretation, risks comparison, and the quadratic hinge loss in SVMs.
Official source
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