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What is Kernel Methods in Machine Learning about?

Kernel methods allow algorithms that depend on dot products between data points to be used with non-linear datasets. This is done by mapping the data points into a higher-dimensional feature space and computing dot products in that space, which can be done efficiently using kernel functions without explicitly computing the coordinates of the mapped points. Common kernel functions include the Gaussian RBF kernel and polynomial kernels. Kernel methods provide a flexible way to apply linear models to non-linea

Author

Pooja Patwari

Language

EN