How did Schölkopf influence ML with kernels?

Kernel methods, particularly their use in Support Vector Machines, offered a powerful way to perform non-linear classification and regression by implicitly mapping data into higher-dimensional spaces. This allowed algorithms to find complex decision boundaries that were not apparent in the original feature space. My work, alongside others, provided a rigorous theoretical framework for understanding why and how these methods succeed.

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