Pool-based active learning in approximate linear regression
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Publication:1959485
DOI10.1007/s10994-009-5100-3zbMath1470.68181OpenAlexW2026386069MaRDI QIDQ1959485
Shinichi Nakajima, Masashi Sugiyama
Publication date: 7 October 2010
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-009-5100-3
covariate shiftapproximate linear regressionimportance-weighted least-squaresALICEpool-based active learning
Linear regression; mixed models (62J05) Learning and adaptive systems in artificial intelligence (68T05)
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Cites Work
- Direct importance estimation for covariate shift adaptation
- Active learning algorithm using the maximum weighted log-likelihood estimator
- Improving predictive inference under covariate shift by weighting the log-likelihood function
- A batch ensemble approach to active learning with model selection
- Robust weights and designs for biased regression models: Least squares and generalized \(M\)-estimation
- Active learning for logistic regression: an evaluation
- Input-dependent estimation of generalization error under covariate shift
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