Matlab code for support vector regression (SVR) and revelance vector regression (RVR) analysis with cross validation to evaluate the prediction power.
Citing our related paper will be greatly appreciated if you use these codes.
Zaixu Cui, Gaolang Gong; The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features, NeuroImage, Volume 178, Pages 622-637
Zaixu Cui, Mengmeng Su, Liangjie Li, Hua Shu, Gaolang Gong; Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume, Cerebral Cortex, Volume 28, Issue 5, 1 May 2018, Pages 1656–1672, https://doi.org/10.1093/cercor/bhx061
Revelance vector Regression (RVR) is implemented using PRoNTo (http://www.mlnl.cs.ucl.ac.uk/pronto/).
The function prt_rvr.m and prt_machine_rvr.m are functions of this software.
Support vector regression (SVR) is implemented using LIBSVM (https://www.csie.ntu.edu.tw/~cjlin/libsvm/).
Copyright (c) Zaixu Cui, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University.
Contact information: zaixucui@gmail.com