Random Fourier features are a technique used to approximate kernel functions in machine learning by mapping input data into a higher-dimensional space using random projections based on Fourier transform principles. This approach enables efficient computations in algorithms that rely on kernels, making it easier to perform tasks like classification and regression while maintaining good generalization properties.
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