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We study the generalization properties of ridge regression with random features in the statistical learning framework.
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
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Estimating the approximation error in learning theory
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Concentration inequalities
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Pattern Recognition and Machine Learning
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Adaptation for regularization operators in learning theory
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An explicit description of the reproducing kernel hilbert spaces of gaussian rbf kernels
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Optimal rates for the regularized least-squares algorithm
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Random features for large-scale kernel machines
A. Rahimi and B. Recht · 2007
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Learning theory estimates via integral operators and their approximations
S. Smale and D. Zhou · 2007
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User-friendly tools for random matrices: An introduction
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Random feature maps for dot product kernels
P. Kar and H. Karnick · 2012
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Efficient additive kernels via explicit feature maps
A. Vedaldi and A. Zisserman · 2012
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Sharp analysis of low-rank kernel matrix approximations
F. Bach · 2013
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Matrix analysis
Rajendra Bhatia · 2013
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Fast and scalable polynomial kernels via explicit feature maps
N. Pham and R. Pagh · 2013
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On the impact of kernel approximation on learning accuracy
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Random laplace feature maps for semigroup kernels on histograms
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Compact random feature maps
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Quasi-monte carlo feature maps for shift-invariant kernels
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Fast randomized kernel ridge regression with statistical guarantees
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Less is more: Nyström computational regularization
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Optimal rates for random fourier features
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On the equivalence between quadrature rules and random features
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