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Kernel methods are ubiquitous tools in machine learning.
A Bayesian Analysis of Some Nonparametric Problems
T. S. Ferguson · 1973
Earlier work this paper cites.
Density estimation for statistics and data analysis , volume 26
B. W. Silverman · 1986
Earlier work this paper cites.
Fourier analysis on groups
W. Rudin · 1990
Earlier work this paper cites.
A constructive definition of Dirichlet priors
J. Sethuraman · 1994
Earlier work this paper cites.
Markov chain sampling methods for Dirichlet process mixture models
R. M. Neal · 1998
Earlier work this paper cites.
Bayesian linear regression
T. P. Minka · 1999
Earlier work this paper cites.
Choosing multiple parameters for support vector machines
O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee · 2002
Earlier work this paper cites.
Sampling techniques for kernel methods
D. Scholkopf, F. Achlioptas, and M. Bernhard · 2002
Earlier work this paper cites.
Bayesian Regression and Classification
C. M. Bishop and M. E. Tipping · 2003
Earlier work this paper cites.
Bayesian Data Analysis
A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin · 2003
Earlier work this paper cites.
Multiple kernel learning, conic duality, and the smo algorithm
F. R. Bach, G. R. Lanckriet, and M. I. Jordan · 2004
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Fast monte-carlo algorithms for finding low-rank approximations
A. Frieze, R. Kannan, and S. Vempala · 2004
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G. R. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan · 2004
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On the nyström method for approximating a gram matrix for improved kernel-based learning
P. Drineas and M. W. Mahoney · 2005
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Random projection, margins, kernels, and feature-selection
A. Blum · 2006
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Fast gaussian process regression using kd-trees
Y. Shen, A. Ng, and M. Seeger · 2006
Cited alongside, same era.
On the complexity of steepest descent, newton’s and regularized newton’s methods for nonconvex unconstrained optimization problems
C. Cartis, N. I. Gould, and P. L. Toint · 2010
Later among the works it cites.
Fourier kernel learning
E. G. Băzăvan, F. Li, and C. Sminchisescu · 2012
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A process over all stationary covariance kernels
A. G. Wilson · 2012
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The mcnemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional
M. W. Fagerland, S. Lydersen, and P. Laake · 2013
Later among the works it cites.
Fastfood–approximating kernel expansions in loglinear time
Q. Le, T. Sarlos, and A. Smola · 2013
Later among the works it cites.
Gaussian process kernels for pattern discovery and extrapolation
A. G. Wilson and R. P. Adams · 2013
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Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
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How to scale up kernel methods to be as good as deep neural nets
Z. Lu, A. May, K. Liu, A. B. Garakani, D. Guo, A. Bellet, L. Fan, M. Collins, B. Kingsbury, M. Picheny, et al · 2014
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