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Bias field reduction by localized Lloyd–Max quantization
Magnetic Resonance Imaging, 2011Bias field reduction is a common problem in medical imaging. A bias field usually manifests itself as a smooth intensity variation across the image. The resulting image inhomogeneity is a severe problem for posterior image processing and analysis techniques such as registration or segmentation.
Mai, Zhenhua +5 more
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Incremental active learning with bias reduction
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000The problem of designing input signals for optimal generalization in supervised learning is called active learning. In many active learning methods devised so far, the bias of the learning results is assumed to be zero. In this paper, we remove this assumption and propose a new active learning method with the bias reduction.
Masashi Sugiyama, Hidemitsu Ogawa
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Antithetic Bias Reduction for Discrete-Event Simulations
Journal of the Operational Research Society, 1987This study presents a technique for reducing the bias induced by arbitrary initial conditions in some discrete simulation studies. The technique relies on compensating the existing bias in a run by purposely introducing a deviation in the counter-direction during the subsequent run. Specifically, after obtaining a sample with initial condition \(X_ 0\),
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Bias Reduction in Logistic Dose-Response Models
Journal of Biopharmaceutical Statistics, 2011In generalized linear models, such as the logistic regression model, maximum likelihood estimators are well known to be biased at smaller sample sizes. When the number of dose levels or replications per dose is small, bias in the maximum likelihood estimates can lead to very misleading results and the model often fails to converge.
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Bias reduction in kernel density estimation
Journal of Nonparametric Statistics, 2018ABSTRACTIn this paper, we propose two kernel density estimators based on a bias reduction technique. We study the properties of these estimators and compare them with Parzen–Rosenblatt's density estimator and Mokkadem, A., Pelletier, M., and Slaoui, Y. (2009, ‘The stochastic approximation method for the estimation of a multivariate probability density’,
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Recycling bias and reduction neglect
Nature Sustainability, 2023Michaela J. Barnett +3 more
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A Survey on Bias and Fairness in Machine Learning
ACM Computing Surveys, 2022Fred Morstatter, Kristina Lerman
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