Figure 1. Inference error versus number of observations. The proportional error (i.e., inference error) denotes the minimal cross-validation error divided by the minimal least-squares error of the linear regression without any regularization terms and averaged over five random networks. The proportional errors decrease with more observations and stabilize when there are enough observations.
Image published in: Zheng Z et al. (2014)
Copyright © 2014 Zheng et al. This image is reproduced with permission of the journal and the copyright holder. This is an open-access article distributed under the terms of the Creative Commons Attribution license
Permanent Image Page
Printer Friendly View
XB-IMG-130674