A support vector machine (SVM) is a supervised machine learning algorithm that analyzes data and learns to categorize it based on past examples. It is commonly used for classification and regression analysis. The algorithm finds the best hyperplane which separates the data into classes by maximizing the margin between them. In other words, it finds the decision boundary that maximizes the distance between the closest data points from each class. The data points that are closest to the decision boundary are known as support vectors, and the distance from the decision boundary to these points is known as the margin. SVMs are popular in many different fields, including image classification, text classification, and bioinformatics.
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