gaussianprocess4Clas

代码说明：

   高斯过程是一种非参数化的学习方法，它可以很自然的用于regression，也可以用于classification。本程序用高斯过程实现分类！(Gaussian process is a non- parametric method of learning, it is very natural for regression. can also be used for classification. The procedures used to achieve classification Gaussian process!)

文件列表：

GPC
...\conffig.m
...\confmat.m
...\conjgrad.m
...\consist.m
...\Contents.m
...\datread.m
...\datwrite.m
...\dem2ddat.m
...\demard.m
...\demev1.m
...\demev2.m
...\demev3.m
...\demgauss.m
...\demglm1.m
...\demglm2.m
...\demgmm1.m
...\demgmm2.m
...\demgmm3.m
...\demgmm4.m
...\demgmm5.m
...\demgp.m
...\demgpard.m
...\demgpot.m
...\demgtm1.m
...\demgtm2.m
...\demhint.m
...\demhmc1.m
...\demhmc2.m
...\demhmc3.m
...\demkmn1.m
...\demknn1.m
...\demmdn1.m
...\demmet1.m
...\demmlp1.m
...\demmlp2.m
...\demnlab.m
...\demns1.m
...\demolgd1.m
...\demopt1.m
...\dempot.m
...\demprgp.m
...\demprior.m
...\demrbf1.m
...\demsom1.m
...\demtrain.m
...\dist2.m
...\eigdec.m
...\errbayes.m
...\evidence.m
...\fevbayes.m
...\gauss.m
...\gbayes.m
...\glm.m
...\glmderiv.m
...\glmerr.m
...\glmevfwd.m
...\glmfwd.m
...\glmgrad.m
...\glmhess.m
...\glminit.m
...\glmpak.m
...\glmtrain.m
...\glmunpak.m
...\gmm.m
...\gmmactiv.m
...\gmmem.m
...\gmminit.m
...\gmmpak.m
...\gmmpost.m
...\gmmprob.m
...\gmmsamp.m
...\gmmunpak.m
...\gp.m
...\gpcovar.m
...\gpcovarf.m
...\gpcovarp.m
...\gperr.m
...\gpfwd.m
...\gpgrad.m
...\gpinit.m
...\gppak.m
...\gpunpak.m
...\GP_classify.m
...\GP_classifydemo.m
...\gradchek.m
...\graddesc.m
...\gsamp.m
...\gtm.m
...\gtmem.m
...\gtmfwd.m
...\gtminit.m
...\gtmlmean.m
...\gtmlmode.m
...\gtmmag.m
...\gtmpost.m
...\gtmprob.m
...\hbayes.m
...\hesschek.m
...\hintmat.m