Support Vector Machines: Definition and Separation Hyperplane

Related lectures (30)

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

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

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

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

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

Explores redescription SVM and Hilbert spaces for solving nonlinear problems using inner product spaces and separable Hilbert spaces.

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

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

Covers the Support Vector Machine with a hard margin formulation and the importance of maximizing the margin between classes.

Covers the Perceptron algorithm and its application to binary classification problems, including the Pocket Perceptron algorithm.