Support vector machine

Related lectures (30)

Page 3 of 3

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

Explores machine learning in brain imaging, focusing on spatial patterns, emotions, and classifier trade-offs.

Explores linear separability in support vector machines, focusing on hyperplane separation and margin optimization.

Covers Support Vector Regression principles, optimization, and hyperparameters' influence on the fit.

Explores Support Vector Machines with a focus on soft margin and multiclass classification using binary classifiers.

Explores Ridge and SVR regression, emphasizing kernel tricks for non-linear regression.

Explores SVM model storage, memory usage, training time complexity, and energy consumption estimation.

Covers the basics of Support Vector Machines, including linear separability, hyperplanes, margins, and non-linear SVM with kernels.

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