Related references
Note: Only part of the references are listed.A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels
Fanghui Liu et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)
Two Models of Double Descent for Weak Features
Mikhail Belkin et al.
SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE (2020)
CROification: Accurate Kernel Classification with the Efficiency of Sparse Linear SVM
Mehran Kafai et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2019)
Reconciling modern machine-learning practice and the classical bias-variance trade-off
Mikhail Belkin et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Optimal rates for coefficient-based regularized regression
Zheng-Chu Guo et al.
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS (2019)
Machine learning and the physical sciences
Giuseppe Carleo et al.
REVIEWS OF MODERN PHYSICS (2019)
A RANDOM MATRIX APPROACH TO NEURAL NETWORKS
Cosme Louart et al.
ANNALS OF APPLIED PROBABILITY (2018)
Distributing many points on spheres: Minimal energy and designs
Johann S. Brauchart et al.
JOURNAL OF COMPLEXITY (2015)
Likelihood approximation by numerical integration on sparse grids
Florian Heiss et al.
JOURNAL OF ECONOMETRICS (2008)
Optimal rates for the regularized least-squares algorithm
A. Caponnetto et al.
FOUNDATIONS OF COMPUTATIONAL MATHEMATICS (2007)
Learning theory estimates via integral operators and their approximations
Steve Smale et al.
CONSTRUCTIVE APPROXIMATION (2007)
Extreme learning machine: Theory and applications
Guang-Bin Huang et al.
NEUROCOMPUTING (2006)
Learning bounds for kernel regression using effective data dimensionality
T Zhang
NEURAL COMPUTATION (2005)
Local Rademacher complexities
PL Bartlett et al.
ANNALS OF STATISTICS (2005)