Results 1 to 10 of about 1,688,491 (311)
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
doaj +4 more sources
CONTRASTIVE LINEAR REGRESSION. [PDF]
Ann Appl StatContrastive dimension reduction methods have been developed for case-control study data to identify variation that is enriched in the foreground (case) data X relative to the background (control) data Y. Here, we develop contrastive regression for the setting when there is a response variable r associated with each foreground observation.
Zhang B +4 more
europepmc +5 more sources
Southwest Respiratory and Critical Care Chronicles, 2014 Gilbert Berdine, Shengping Yang
doaj +2 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
A mixture of linear-linear regression models for a linear-circular regression [PDF]
Statistical Modelling, 2019 We introduce a new approach to a linear-circular regression problem that relates multiple linear predictors to a circular response. We follow a modelling approach of a wrapped normal distribution that describes angular variables and angular distributions and advances them for a linear-circular regression analysis.
Sikaroudi, Ali Esmaieeli, Park, Chiwoo
openaire +3 more sources
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.
Andrew S. Lan +2 more
openaire +2 more sources
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
doaj +1 more source
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
doaj +1 more source