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...

Traditional Symbolic Regression applications are a form of supervised learning, where a label y is provided for every ~x and an explicit symbolic relationship of the form y = f(~x) is sought. This chapter explores the use of symbolic regression to perform unsupervised learning by searching for implicit relationships of the form f(~x, y) = 0. Implicit relationships are more general and more expr...

Computational fluid dynamics (CFD) analysis is widely used in chemical engineering. Although CFD calculations are accurate, the computational cost associated with complex systems makes it difficult to obtain empirical equations between system variables. Here, we combine active learning (AL) and symbolic regression (SR) get a equation for variables from simulations. Gaussian process regression-b...

the acute ecotoxicity of a set of mixture samples (washing powders, wastes, fuel extracts etc.) was assessed using four acute ecotoxicity assays with aquatic organisms (daphnia magna, poecilia reticulata, artemia salina and desmodesmus subspicatus). the experimental concentration-response curves were fitted by seven two-parameter sigmoid equations using non-linear regression. the regression per...

introduction:expensive chemical tests are usuallyapplied to control the efficiency of wastewater treatment plants. however, the cost of measuring these parameters is one of the challenges of the wastewater treatment plants. using statistical methods has been evaluated for possibility of calculating expensive parameters by measuring the inexpensive parameters in this study. methods:initial sampl...