Support Vector Machines: Exercises Solutions

Related lectures (32)

Page 3 of 4

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

Explores binary classification by regression, decision functions, and various cost functions.

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

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

Covers the formalism for supervised learning and decision functions in classification problems.

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

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

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Covers supervised learning, decision theory, risk minimization, and goal achievement.

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