Matlab code for support vector regression (SVR) and revelance vector regression (RVR) analysis with cross validation to evaluate the prediction power.
Also see the codes here https://github.com/ZaixuCui/Pattern_Regression_Clean.
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, (2018), NeuroImage, 178: 622-37
Zaixu Cui, et al., Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume, (2018), Cerebral Cortex, 28(5):1656–72
Zaixu Cui, et al., Individual variation in functional topography of association networks in youth. (2020) Neuron, 106(2): 340-53.
Zaixu Cui, et al., Optimization of energy state transition trajectory supports the development of executive function during youth. (2020) eLife. 9:e53060.
Zaixu Cui, et al., 2016. Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach. Hum Brain Mapp 37, 1443-1458.
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