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A class of discriminative classifiers defined by a separating hyperplane. For each labeled training data point, an SVM algorithm identifies the optimal hyperplane that maximizes the margin between different classes. This hyperplane is used to categorize new, unseen data points. SVMs are particularly effective in high-dimensional spaces and are widely used for classification tasks, including text classification and image recognition.