Support vector machine (SVM) is a type of machine learning algorithm that can be used for classification and regression tasks. They build upon basic ML algorithms and add features that make them more efficient at various tasks.
Support vector machines can be used in a variety of tasks, including anomaly detection, handwriting recognition, and text classification. Because of their flexibility, high performance, and compute efficiency, SVMs have become a mainstay of machine learning and an important addition to the ML engineer’s toolbox.
Support vector machines are among supervised machine learning algorithms, which means they need to be trained on labeled data.
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