Regression methods aim at inducing models of numeric data. While most state-of-the-art machine learning methods for regression focus on inducing piecewise regression models (regression and model trees), we investigate the predictive performance of regression models based on polynomial equations. We present Ciper, an efficient method for inducing polynomial equations and empirically evaluate its...

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...

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...