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Computer Science > Machine Learning

arXiv:1710.10230 (cs)
[Submitted on 27 Oct 2017 (v1), last revised 27 Feb 2018 (this version, v2)]

Title:Not-So-Random Features

Authors:Brian Bullins, Cyril Zhang, Yi Zhang
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Abstract:We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels. Our method produces a sequence of feature maps, iteratively refining the SVM margin. We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game. Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.
Comments: Published as a conference paper at ICLR 2018
Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:1710.10230 [cs.LG]
  (or arXiv:1710.10230v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1710.10230

Submission history

From: Cyril Zhang [view email]
[v1] Fri, 27 Oct 2017 16:28:06 UTC (2,924 KB)
[v2] Tue, 27 Feb 2018 00:50:27 UTC (7,388 KB)
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