Fastfood: Approximate Kernel Expansions in Loglinear Time — mentioned by FreakLegion

Sat, 12 Nov 2022 00:45:30 GMT

Of course. The paper is at https://arxiv.org/abs/1408.3060. > Our method applies to any translation invariant and any dot-product kernel, such as the popular RBF kernels and polynomial kernels. We prove that the approximation is unbiased and has low variance. Experiments show that we achieve similar accuracy to full kernel expansions and Random Kitchen Sinks while being 100x faster and using 1000x less memory. These improvements, especially in terms of memory usage, make kernel methods more practical for applications that have large training sets and/or require real-time prediction. Sadly…

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Fastfood: Approximate Kernel Expansions in Loglinear Time · HackerPapers discussions

Fastfood: Approximate Kernel Expansions in Loglinear Time — mentioned by FreakLegion