S U M M A R Y Least-squares linear regression is so popular that it is sometimes applied without checking whether its basic requirements are satisfied. In particular, in studying earthquake phenomena, the conditions (a) that the uncertainty on the independent variable is at least one order of magnitude smaller than the one on the dependent variable, (b) that both data and uncertainties are norm...

The paper considcrs data modelling using multi-output regression models. A locally regularised orthogonal least-squares (LROLS) algorithm is proposed for constructing sparse multi-output regression models that generalise well. By associating each regressor in the regression model with an individual regularisation parameter, the ability of the multi-output orthogonal least-squares (OLS) model se...

В роботі досліджуються питання побудови регресійних та емпіричних залежностей для вивчення процесів, які описуються часовими рядами дискретних даних.

this work presents an experimental investigation of the influence of the six important machining parameters (tool nose radius, tool rake angle, feed rate, cutting speed, cutting environment (dry, wet and cooled) and depth of cut) on surface roughness & material removal rate in the machining unidirectional glass fiber reinforced plastics (ud-gfrp) composite using carbide (k10) cutting tool durin...

A new iterative orthogonal least squares forward regression (iOFR) algorithm is proposed to identify nonlinear systems which may not be persistently excited. By slightly revising the classic forward orthogonal regression (OFR) algorithm, the new iterative algorithm provides search solutions on a global solution space. Examples show that the new iterative algorithm is computationally efficient a...

A novel training algorithm for nonlinear discriminants for classification and regression in Reproducing Kernel Hilbert Spaces (RKHSs) is presented. It is shown how the overdetermined linear leastsquares-problem in the corresponding RKHS may be solved within a greedy forward selection scheme by updating the pseudoinverse in an order-recursive way. The described construction of the pseudoinverse ...