Support Vector Machines: SVM Basics

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Page 1 of 3

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

Explains SVM for non-separable datasets, introducing slack variables and optimizing the margin for classification.

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

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

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Explains how to find the best hyperplane to separate two classes.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

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

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

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.