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What is Svms Kernels about?

The document discusses the foundations of Support Vector Machines (SVMs) and the use of kernel functions to learn non-linear patterns. It presents the optimization problem for soft-margin SVMs, the dual form of the SVM optimization, and how kernels can be used to map data into higher-dimensional spaces for improved classification. Various kernel functions such as linear, quadratic, polynomial, and radial basis function (RBF) are introduced, along with their application in kernelized SVMs.

Author

mnghiftpilmtwporsq

Language

EN