PVM is a fast supvervised leanring algorithm who combine dimensioin reduction and neural network training.

I have prepared the code (including six algorithms KPVM, EL M/SVD, BP/SVD and BPVM, and one dataset "Face") and put them in one zip file "pvm_code.zip", you can unzip it and run "Face_mean.m" function in Matlab environment . Before carried out experiments, please include “DimReduce” and “IncPACK” package (in the pvm_code folder)in the Matlab path setting. You will see Avarage Training time, Avarage Testing Time, Avarage Training Accuracy and Avarage Testing Accuracy of 50 trials. Because some dataset is large even they are zipped, so we just upload one dataset "Face". Other dataset can be downloaded from UCI website. According the parameter settings listed in Table 2 and Table 9, you can get the experimental results. But because the dataset is randomly split in each trial, the result may be slightly different. -I have prepared the code (including six algorithms KPVM, ELM, ELM/SVD, BP/SVD and BPVM, and one dataset "Face") and put them in one zip file "pvm_code.zip", you can unzip it and run "Face_mean.m" function in Matlab environment . Before carried out experiments, please include “DimReduce” and “IncPACK” package (in the pvm_code folder)in the Matlab path setting. You will see Avarage Training time, Avarage Testing Time, Avarage Training Accuracy and Avarage Testing Accuracy of 50 trials. Because some dataset is large even they are zipped, so we just upload one dataset "Face". Other dataset can be downloaded from UCI website. According the parameter settings listed in Table 2 and Table 9, you can get the experimental results. But because the dataset is randomly split in each trial, the result may be slightly different.