Support Vector Machines: Hyperparameters and V-SVM

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Covers the basics of Support Vector Machines, focusing on hard-margin and soft-margin formulations.

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

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.

Covers the derivation of Linear Support Vector Machine (SVM) and the Karush-Kuhn-Tucker (KKT) conditions.

Explains how to find the best hyperplane to separate two classes.

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

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

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

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