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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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Southwest Respiratory and Critical Care Chronicles, 2014 Gilbert Berdine, Shengping Yang
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(Non) Linear Regression Modeling [PDF]
, 2004 We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1, . . .
Čížek, Pavel
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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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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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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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Linearized binary regression [PDF]
2018 52nd Annual Conference on Information Sciences and Systems (CISS), 2018 Probit regression was first proposed by Bliss in 1934 to study mortality rates of insects. Since then, an extensive body of work has analyzed and used probit or related binary regression methods (such as logistic regression) in numerous applications and fields.
Lan, Andrew S. +2 more
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Tensor Linear Regression: Degeneracy and Solution
IEEE Access, 2021 Tensor regression is an important and useful tool for analyzing multidimensional array data. To deal with high dimensionality, CANDECOMP/PARAFAC (CP) low-rank constraints are often imposed on the coefficient tensor parameter in the (penalized) loss ...
Ya Zhou, Raymond K. W. Wong, Kejun He
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Secure Collaborative Computing for Linear Regression
Applied Sciences, 2023 Machine learning usually requires a large amount of training data to build useful models. We exploit the mathematical structure of linear regression to develop a secure and privacy-preserving method that allows multiple parties to collaboratively compute
Albert Guan, Chun-Hung Lin, Po-Wen Chi
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