Support Vector Machines: SVMs

Related lectures (28)

Page 1 of 3

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

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

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

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

Explains SVM for non-separable datasets, introducing slack variables and optimizing the margin for classification.

Gives an overview of Support Vector Machines, comparing advantages and disadvantages of SVM with other classifiers.

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

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