Created by W.Langdon from gp-bibliography.bib Revision:1.7964
Genetic programming is used to develop non-linear models of functional connectivity from fMRI data. The study builds on previous work and observes that non linear models contain relationships not found by traditional linear methods. When compared to linear models, the nonlinear models contained fewer regions of interest and were never significantly worse when applied to data the models were fit to. Nonlinear models could generalize to unseen data from the same subject better than traditional linear models (intra-subject). Nonlinear models could not generalize to unseen data recorded from other subjects (intersubject) as well as the linear models, and reasons for this are discussed. This study presents the problem that many, manifestly different models in both operators and features, can effectively describe the system with acceptable metrics.",
IEEE Catalog Number: CFP19ICE-ART",
Genetic Programming entries for James Alexander Hughes Mark Daley