文件名称:gaussianprocess4Clas
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 284kb
- 下载次数：
- 0次
- 提 供 者：
- wuyu****
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
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!
相关搜索： 高斯过程
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下载文件列表
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
...\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