fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...

Typical scenarios occurring in genomics and proteomics involve small number of samples and large number of variables. Thus, variable selection is necessary for creating disease prediction models robust to overfitting. We propose an unsupervised variable selection method based on sparseness constrained decomposition of a sample. Decomposition is based on nonlinear mixture model comprised of test...

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-lineari...

Quantile regression (QR) has become a popular method of data analysis, especially when the error term is heteroscedastic, due to its relevance in many scientific studies. The ubiquity of high dimensional data has led to a number of variable selection methods for linear/nonlinear QR models and, recently, for the single index quantile regression (SIQR) model. We propose a new algorithm for simult...

Research into regularization techniques is motivated by the tendency of neural networks to to learn the specifics of the dataset it was trained on rather than learning general features that are applicable to unseen data. This is known as overfitting. The goal of any supervised machine learning task is to approximate a function that maps inputs to outputs, given a dataset of examples and labels....

Immediate roof caving in longwall mining is a complex dynamic process, and it is the core of numerous issues and challenges in this method. Hence, a reliable prediction of the strata behavior and its caving potential is imperative in the planning stage of a longwall project. The span of the main caving is the quantitative criterion that represents cavability. In this paper, two approaches are p...

Two experiments investigated the mental representation of spatial descriptions. In Experiment 1, the subjects classified a series of diagrams, each presented after a spatial description, as either consistent or inconsistent with the description. They were then given an unexpected recognition test of their memory for the descriptions. The subjects remembered the meanings of determinate descripti...

an analysis of the stages of crop growth presents an important step in the improvement of production management. through growth analysis, planning for planting systems, fertilization, pruning operations, harvest time as well as obtaining economical yield can be more accessible. therefore, a development of mathematical models simulating plant growth would be helpful in improving crop management,...