Semi-supervised speaker identification under covariate shift

DOI10.1016/j.sigpro.2009.06.001zbMath1194.94154OpenAlexW2098766654MaRDI QIDQ985576

Makoto Yamada, Masashi Sugiyama, Tomoko Matsui

Publication date: 6 August 2010

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sigpro.2009.06.001

zbMATH Keywords

semi-supervised learningkernel logistic regressionspeaker identificationcovariate shiftimportance estimation

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items

Adapting a classification rule to local and global shift when only unlabelled data are available, Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search, High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso, Safe semi-supervised learning based on weighted likelihood

Uses Software

• PRMLT

Cites Work

• Direct importance estimation for covariate shift adaptation
• A kernel trick for sequences applied to text-independent speaker verification systems
• Active learning algorithm using the maximum weighted log-likelihood estimator
• Improving predictive inference under covariate shift by weighting the log-likelihood function
• Adaptive importance sampling for value function approximation in off-policy reinforcement learning
• Robust weights and designs for biased regression models: Least squares and generalized \(M\)-estimation
• Sample Selection Bias as a Specification Error
• Trading Variance Reduction with Unbiasedness: The Regularized Subspace Information Criterion for Robust Model Selection in Kernel Regression
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