Kernel trick

The kernel trick is a method used in machine learning that allows learning algorithms to operate in a high-dimensional space without explicitly computing the coordinates of the data in that space. It works by using a kernel function to compute the inner products of the data in the high-dimensional feature space, thus enabling algorithms like Support Vector Machines (SVM) to classify data that is not linearly separable in the original space.