Statistical analysis of kernel-based least-squares density-ratio estimation

DOI10.1007/s10994-011-5266-3zbMath1246.68182OpenAlexW1974314970MaRDI QIDQ420923

Taiji Suzuki, Takafumi Kanamori, Masashi Sugiyama

Publication date: 23 May 2012

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-011-5266-3

zbMATH Keywords

classificationconvergence ratekernel methodsimulationsdensity ratio

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Semi-supervised learning with density-ratio estimation, Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation, Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation, Distributed computation for marginal likelihood based model choice, Computational complexity of kernel-based density-ratio estimation: a condition number analysis, Density-Difference Estimation, Semi-supervised learning of class balance under class-prior change by distribution matching, Least-squares independence regression for non-linear causal inference under non-Gaussian noise, Unnamed Item, Change-point detection in time-series data by relative density-ratio estimation, Machine learning with squared-loss mutual information, Variable Selection for Nonparametric Learning with Power Series Kernels, Imitation Learning as f-Divergence Minimization, Semiparametric optimal estimation with nonignorable nonresponse data, On a regularization of unsupervised domain adaptation in RKHS, Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules, Information-Theoretic Representation Learning for Positive-Unlabeled Classification, Numerical study of learning algorithms on Stiefel manifold

Uses Software

• R

Cites Work

• Direct importance estimation for covariate shift adaptation
• Linear and nonlinear programming.
• Convergence rates of generalization errors for margin-based classification
• Improving predictive inference under covariate shift by weighting the log-likelihood function
• Nonparametric and semiparametric models.
• Computational complexity of kernel-based density-ratio estimation: a condition number analysis
• Some results on Tchebycheffian spline functions and stochastic processes
• On the mathematical foundations of learning
• 10.1162/153244302760185252
• Density Ratio Estimation in Machine Learning
• Remarks on Some Nonparametric Estimates of a Density Function
• Consistency of Support Vector Machines and Other Regularized Kernel Classifiers
• Input-dependent estimation of generalization error under covariate shift
• Inferences for case-control and semiparametric two-sample density ratio models
• Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization
• Convexity, Classification, and Risk Bounds
• Theory of Reproducing Kernels
• A fast dual algorithm for kernel logistic regression
• Soft margins for AdaBoost
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item