Kernel methods are a class of algorithms used in machine learning and statistics that rely on the concept of a "kernel" function. These methods are particularly useful for handling non-linear data by implicitly mapping data into a higher-dimensional feature space without the need for explicit transformation. This approach allows linear algorithms to be applied to data that is not linearly separable in its original space.
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A kernel method represents data through a positive-semidefinite kernel, allowing algorithms to use inner products in an implicit feature map.