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MACHINE LEARNING Support Vector Machines (SVM) is a supervised machine learning algorithm introduced by Vladimir N. Vapnik and his colleagues in the 1990s. It excels in classification tasks by identifying an optimal hyperplane that maximizes the margin between classes, ensuring robust performance on unseen data. Leveraging the kernel trick, SVM can handle both linear and nonlinear classification, using various kernel functions like linear, polynomial, radial basis function (RBF), and sigmoid to adapt to diverse data patterns Nora Yehia MACHINE LEARNING Collaborative Filtering…
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