The Kernel Trick, in the context of Support Vector Machines (SVM), is a mathematical technique that allows us to transform a linear classification problem into a high-dimensional nonlinear space, where the separator can be calculated more easily. This is achieved by defining a function called a kernel which maps the input data into a higher-dimensional space, where it can be separated using a linear separator.
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