ABSTRACT: The solar data used to size installations for energy needs are most often oversized. The data used are either old or suffer from the effects of climate change or from data extrapolated to a ...
Abstract: Mixed linear regression (MLR) models nonlinear data as a mixture of linear components. When noise is Gaussian, the Expectation-Maximization (EM) algorithm is commonly used for maximum ...
The goal of liu.lab4.algorithms is to provide an R implementation of a multiple linear regression mode. This package was created for Lab 4 in the course 732A94 Advanced R Programming at Linköping ...
ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...
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This study introduces an XGBoost-MICE (Multiple Imputation by Chained Equations) method for addressing missing data in mine ventilation parameters. Using historical ventilation system data from ...
Abstract: In this paper, we consider the problem of learning a linear regression model on a data domain of interest (target) given few samples. To aid learning, we are provided with a set of ...
The aim of the present study was to establish a predictive model to predict the peritoneal cancer index (PCI) preoperatively in patients with pseudomyxoma peritonei (PMP). This study represents the ...
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