Support Vector Machines

A strong machine learning technique called Support Vector Machine (SVM) is utilized for applications including regression, outlier detection, and linear or nonlinear classification. Text classification, picture classification, spam detection, handwriting recognition, gene expression analysis, face detection, and anomaly detection are just a few of the tasks that SVMs can be utilized. SVMs can handle high-dimensional data and nonlinear relationships, making them flexible and effective in a wide range of applications. To locate the largest separation hyperplane between the many classes included in the target feature, SVM methods are particularly successful. Useful in cases with big dimensions. The decision function's memory usage is efficient since it makes use of support vectors, a subset of training points. For the decision functions, various kernel functions can be supplied, as well as specific kernels.

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