LIBSVM

Related lectures (30)

Page 1 of 3

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 Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Explores Support Vector Machines theory, parameters, uniqueness, and applications in machine learning.

Explores SVM parameters, solutions, and decision boundaries, including the uniqueness of solutions and the impact of kernel width.

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Explores transforming data with nonlinear maps, kernels, dual optimization, and interpreting SVM results.

Delves into SVM hyperparameters, showcasing the impact of C and kernel width on classification results.