Max-Margin Classifiers

Related lectures (30)

Page 1 of 3

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

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

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

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

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

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

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

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.