Support Vector Machines (SVMs) are a type of machine learning algorithm used for classification and regression tasks. SVMs work by finding the best hyperplane that separates the data into different classes or groups. In SVMs, the hyperplane is chosen so as to maximize the margin between the classes, which is the distance between the hyperplane and the closest data points from each class. The data points that are closest to the hyperplane are known as support vectors.
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