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Support Vector Machines
The document discusses support vector machines (SVMs) and how they find the maximum margin linear classifier to classify data. Specifically, it explains that SVMs: 1) Find the linear decision boundary that maximizes the margin or distance between the boundary and the closest data points of each class. 2) The maximum margin classifier is the simplest type of SVM called a linear SVM (LSVM). 3) The margin is computed in terms of the weights w and bias b that define the decision boundary. Maximizing this margin leads to the optimal separating hyperplane.
