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Adiabatic quantum linear regression [PDF]
Scientific Reports, 2021 A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on ...
Prasanna Date, Thomas Potok
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Linear regression analysis study
Journal of the Practice of Cardiovascular Sciences, 2018 Linear regression is a statistical procedure for calculating the value of a dependent variable from an independent variable. Linear regression measures the association between two variables.
Khushbu Kumari, Suniti Yadav
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Application and interpretation of linear-regression analysis. [PDF]
Med Hypothesis Discov Innov OphthalmolBackground: Linear-regression analysis is a well-known statistical technique that serves as a basis for understanding the relationships between variables.
Roustaei N.
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Regression Analysis with Scikit-Learn (part 1 - Linear)
The Programming Historian, 2022 This lesson is the first of a two-part lesson focusing on an indispensable set of data analysis methods, logistic and linear regression. It provides an overview of linear regression and walks through running both algorithms in Python (using scikit-learn).
Matthew J. Lavin
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Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation and Minimax Optimality [PDF]
Journal of The Royal Statistical Society Series B-statistical Methodology, 2020 This paper considers estimation and prediction of a high‐dimensional linear regression in the setting of transfer learning where, in addition to observations from the target model, auxiliary samples from different but possibly related regression models ...
Sai Li, T. Cai, Hongzhe Li
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Function-on-function linear quantile regression
Mathematical Modelling and Analysis, 2022 In this study, we propose a function-on-function linear quantile regression model that allows for more than one functional predictor to establish a more flexible and robust approach. The proposed model is first transformed into a finitedimensional space
Ufuk Beyaztas, Han Lin Shang
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Benign overfitting in linear regression [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2019 The phenomenon of benign overfitting is one of the key mysteries uncovered by deep learning methodology: deep neural networks seem to predict well, even with a perfect fit to noisy training data.
P. Bartlett +3 more
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Post-processing through linear regression [PDF]
Nonlinear Processes in Geophysics, 2011 Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed,
B. Van Schaeybroeck, S. Vannitsem
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Knowledge and Awareness: Linear Regression [PDF]
Educational Process: International Journal, 2016 Knowledge and awareness are factors guiding development of an individual. These may seem simple and practicable, but in reality a proper combination of these is a complex task.
Monika Raghuvanshi
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Differentially Private Simple Linear Regression [PDF]
Proceedings on Privacy Enhancing Technologies, 2020 Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data.
Daniel Alabi +4 more
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