Variable Selection for Nonparametric Learning with Power Series Kernels

DOI10.1162/neco_a_01212zbMath1435.62136arXiv1806.00569OpenAlexW2805257271WikidataQ91508578 ScholiaQ91508578MaRDI QIDQ5214363

Kenta Kanamori, Mitsuaki Nishikimi, Takafumi Kanamori, Wataru Kumagai, Kota Matsui

Publication date: 7 February 2020

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.00569

zbMATH Keywords

variable selectionnonparametric learning

Mathematics Subject Classification ID

Density estimation (62G07) Ridge regression; shrinkage estimators (Lasso) (62J07) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• UCI-ml
• Scikit

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• Statistical analysis of kernel-based least-squares density-ratio estimation
• Power series kernels
• Derivative reproducing properties for kernel methods in learning theory
• Improving predictive inference under covariate shift by weighting the log-likelihood function
• Optimal rates for the regularized least-squares algorithm
• Density Ratio Estimation in Machine Learning
• Nonparametric sparsity and regularization
• Better Subset Regression Using the Nonnegative Garrote
• Support Vector Machines
• Asymptotic Statistics
• 10.1162/153244303322753616
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• Flexible variable selection for recovering sparsity in nonadditive nonparametric models
• Density Estimation in Infinite Dimensional Exponential Families
• Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery
• On the Non-Negative Garrotte Estimator