Support vectors are the data points in a support vector machine (SVM) that are closest to the decision boundary, or hyperplane, used for classification. These points are critical because they influence the position and orientation of the hyperplane, ultimately determining how well the SVM can separate different classes in the dataset. In essence, support vectors are the key elements that help define the SVM model's performance and robustness.
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