Linear SVM derivation

Related lectures (29)

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Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Explores the dual formulation of Support Vector Machines for hard margin classification.

Explores Support Vector Machines, maximizing margin for robust classification and the transition to soft SVM for non-linearly separable data.

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Covers linear models for classification, focusing on logistic regression and support vector machines.