LIBSVM

Related lectures (30)

Page 3 of 3

Covers the history, linear separability, hyperplanes, and support vectors in Support Vector Machines.

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

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

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

Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.

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

Explores polynomial expansion, high-dimensional spaces, Cover's Theorem, Lagrangian formulation, and practical SVM applications.

Covers polynomial feature expansion, kernel functions, regression, and SVM, emphasizing the importance of choosing functions for feature expansion.

Covers the basics of Support Vector Machines and Lagrange Duality.

Explores convexifying nonconvex problems through SVM and dimensionality reduction techniques.