Web: http://arxiv.org/abs/2201.08712

Jan. 24, 2022, 2:10 a.m. | Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone

cs.LG updates on arXiv.org arxiv.org

Dot product kernels, such as polynomial and exponential (softmax) kernels,
are among the most widely used kernels in machine learning, as they enable
modeling the interactions between input features, which is crucial in
applications like computer vision, natural language processing, and recommender
systems. We make several novel contributions for improving the efficiency of
random feature approximations for dot product kernels, to make these kernels
more useful in large scale learning. First, we present a generalization of
existing random feature approximations …

arxiv ml product random

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