File:Ting et al 2017 LWRControl Fig1.png

Locally Weighted Regression for Control, Fig. 1. Function approximation results for the function [math]\displaystyle{ y= sin(2x) + 2 \exp( -16^2x) + N(0, 0.16/) }[/math]with (a) a sigmoidal neural network, (b) a locally weighted regression algorithm (note that the data traces “true y,” “predicted y,” and “predicted y after new training data” largely coincide), and (c) the organization of the (Gaussian) kernels of (b) after training. See Schaal and Atkeson 1998 for more details. In: Jo-Anne Ting, Franzisk Meier, Sethu Vijayakumar, and Stefan Schaal (2017) "Locally Weighted Regression for Control". In: Sammut & Webb (2017).