A Support Vector Machine (SVM) is a supervised machine learning model that uses algorithms for classification and regression analysis. SVMs are effective when the number of dimensions is greater than the number of samples. SVMs are versatile: different Kernel functions can be specified for the decision function. Common kernels include linear, polynomial, radial basis function (RBF), and sigmoid.
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