Results 61 to 70 of about 2,584,664 (216)
Regression Models for Lean Production [PDF]
, 2017 Data mining models are an excellent tool to help companies that live from the sales of items they produce because it allows the company to optimize its production and reduce costs, for example in storage. When these models are combined with Lean Production, it becomes easier to remove waste and optimize industrial production.
Ricardo Bragança +2 more
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Regression Models of Atlas Appearance [PDF]
, 2009 Models of object appearance based on principal components analysis provide powerful and versatile tools in computer vision and medical image analysis. A major shortcoming is that they rely entirely on the training data to extract principal modes of appearance variation and ignore underlying variables (e.g., subject age, gender).
Torsten Rohlfing +2 more
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Indian Journal of Health Sciences and Biomedical Research KLEUINTRODUCTION: Children's health is tomorrow's wealth is one of the WHO's slogans of the recent years. However, children's health is to a great extent determined by factors that operate in utero, well before they are born. Newborns falling in the category
Praveen Ganganahalli +3 more
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Using the classical linear regression model in analysis of the dependences of conveyor belt life [PDF]
Acta Montanistica Slovaca, 2013 The paper deals with the classical linear regression model of the dependence of conveyor belt life on some selected parameters: thickness of paint layer, width and length of the belt, conveyor speed and quantity of transported material. The first part of
Miriam Andrejiová, Daniela Marasová
doaj
Algorithms, 2018 The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket).
Yamur K. Al-Douri +2 more
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A simple bivariate count data regression model [PDF]
This paper develops a simple bivariate count data regression model in which dependence between count variables is introduced by means of stochastically related unobserved heterogeneity components. Unlike existing commonly used bivariate models, we obtain
Shiferaw Gurmu, John Elder
core
Graph Huber: a robust regression model for graph data
Xibei Gongye Daxue XuebaoAs it is increasingly prevalent that data contains noise or obeys heavy-tailed distribution, a robust regression model becomes one of focal and hot topics in many study fields.
SU Meihong +3 more
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Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing
Remote Sensing, 2019 Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the ...
Liyuan Zhang +3 more
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Nonparametric estimation of an additive quantile regression model [PDF]
This paper is concerned with estimating the additive components of a nonparametric additive quantile regression model. We develop an estimator that is asymptotically normally distributed with a rate of convergence in probability of n-r/(2r+1) when the ...
Sokbae 'Simon' Lee, Joel Horowitz
core
Bayesian Regularisation in Structured Additive Regression Models for Survival Data [PDF]
, 2008 During recent years, penalized likelihood approaches have attracted a lot of interest both in the area of semiparametric regression and for the regularization of high-dimensional regression models.
Konrath, Susanne +2 more
core +1 more source