The paper addresses the task of multi-target polynomial regression, i.e., the task of inducing polynomials that can predict the value of more then one numeric variable. As in other learning tasks, we face the problem of finding an optimal trade-off between the complexity of the induced model and its predictive error. We propose a minimal description length scheme for multi-target polynomial reg...

In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of t...

This paper introduces a new local polynomial estimator and develops supporting asymptotic theory for non-parametric regression in the presence of covariate measurement error. We address the measurement error with Cook and Stefanski’s simulation-extrapolation (SIMEX) algorithm. Our method improves on previous local polynomial estimators for this problem by (1) using a bandwidth selection procedu...

Recycling and reusing plastic waste are key options for ameliorating its negative effects on the environment economy. This paper proposes evolutionary polynomial regression (EPR) as a powerful technique to predict compressibility behavior of sand high-density polyethylene (HDPE) mixtures. In investigation, series large-scale oedometer experiments were conducted. The results coefficients lateral...

background: forecasting of air pollutants has become a popular topic of environmental research today. for this purpose, the artificial neural network (aan) technique is widely used as a reliable method for forecasting air pollutants in urban areas. on the other hand, the evolutionary polynomial regression (epr) model has recently been used as a forecasting tool in some environmental issues. in ...

In this paper we review and discuss some of the theoretical and practical aspects, the problems, and the considerations that pushed our research from the one-dimensional LPA-ICI (local polynomial approximation interesection of conÞdence intervals) algorithm [27] to the development of powerful transform-based methods for anisotropic image processing [18, 15, 19, 20, 8, 21]. In this paper we do n...

this study was conducted to investigate the effects of different temperatures on seed germination of plantago ovata and also to evaluate cardinal temperatures. various constant temperatures (5, 10, 15, 20, 25, 30 and 35ºc) were conducted in a completely randomized design with four replications. the germinated seeds were counted every day and the rate of germination percentage, cumulative germin...

The Nadaraya–Watson estimator, also known as kernel regression, is a density-based regression technique. It weights output values with the relative densities in input space. The density is measured with kernel functions that depend on bandwidth parameters. In this work we present an evolutionary bandwidth optimizer for kernel regression. The approach is based on a robust loss function, leave-on...