function?[sig_e?baymodel]?=?bay_errorbar（modelXt?type?nb?bay）
%?Compute?the?error?bars?for?a?one?dimensional?regression?problem
%?
%?>>?sig_e?=?bay_errorbar（{XY‘function‘gamsig2}?Xt）
%?>>?sig_e?=?bay_errorbar（model?Xt）
%?
%?The?computation?takes?into?account?the?estimated?noise?variance
%?and?the?uncertainty?of?the?model?parameters?estimated?by
%?Bayesian?inference.?sig_e?is?the?estimated?standard?deviation?of
%?the?error?bars?of?the?points?Xt.?A?plot?is?obtained?by?replacing
%?Xt?by?the?string?‘figure‘.
%?
%
%?Full?syntax
%?
%?????1.?Using?the?functional?interface:
%?
%?>>?sig_e?=?bay_errorbar（{XY‘function‘gamsig2kernelpreprocess}?Xt）
%?>>?sig_e?=?bay_errorbar（{XY‘function‘gamsig2kernelpreprocess}?Xt?type）
%?>>?sig_e?=?bay_errorbar（{XY‘function‘gamsig2kernelpreprocess}?Xt?type?nb）
%?>>?sig_e?=?bay_errorbar（{XY‘function‘gamsig2kernelpreprocess}?‘figure‘）
%?>>?sig_e?=?bay_errorbar（{XY‘function‘gamsig2kernelpreprocess}?‘figure‘?type）
%?>>?sig_e?=?bay_errorbar（{XY‘function‘gamsig2kernelpreprocess}?‘figure‘?type?nb）
%?
%???????Outputs????
%?????????sig_e?????????:?Nt?x?1?vector?with?the?[$?\sigma^2$]?errorbands?of?the?test?data
%???????Inputs????
%?????????X?????????????:?N?x?d?matrix?with?the?inputs?of?the?training?data
%?????????Y?????????????:?N?x?1?vector?with?the?inputs?of?the?training?data
%?????????type??????????:?‘function?estimation‘?（‘f‘）
%?????????gam???????????:?Regularization?parameter
%?????????sig2??????????:?Kernel?parameter
%?????????kernel（*）?????:?Kernel?type?（by?default?‘RBF_kernel‘）
%?????????preprocess（*）?:?‘preprocess‘（*）?or?‘original‘
%?????????Xt????????????:?Nt?x?d?matrix?with?the?inputs?of?the?test?data
%?????????type（*）???????:?‘svd‘（*）?‘eig‘?‘eigs‘?or?‘eign‘
%?????????nb（*）?????????:?Number?of?eigenvalues/eigenvectors?used?in?the?eigenvalue?decomposition?approximation
%
%?????2.?Using?the?object?oriented?interface:
%?
%?>>?[sig_e?bay?model]?=?bay_errorbar（model?Xt）
%?>>?[sig_e?bay?model]?=?bay_errorbar（model?Xt???????type）
%?>>?[sig_e?bay?model]?=?bay_errorbar（model?Xt???????type?nb）
%?>>?[sig_e?bay?model]?=?bay_errorbar（model?‘figure‘）
%?>>?[sig_e?bay?model]?=?bay_errorbar（model?‘figure‘?type）
%?>>?[sig_e?bay?model]?=?bay_errorbar（model?‘figure‘?type?nb）
%?
%???????Outputs????
%?????????sig_e?????:?Nt?x?1?vector?with?the?[$?\sigma^2$]?errorbands?of?the?test?data
%?????????model（*）??:?object?oriented?representation?of?the?LS-SVM?model
%?????????bay（*）????:?object?oriented?representation?of?the?results?of?the?Bayesian?inference
%???????Inputs????
%?????????model?????:?object?oriented?representation?of?the?LS-SVM?model
%?????????Xt????????:?Nt?x?d?matrix?with?the?inputs?of?the?test?data
%?????????type（*）???:?‘svd‘（*）?‘eig‘?‘eigs‘?or?‘eign‘
%?????????nb（*）?????:?Number?of?eigenvalues/eigenvectors?used?in?the?eigenvalue?decomposition?approximation
%?
%?See?also:
%???bay_lssvm?bay_optimize?bay_modoutClass?plotlssvm

%?Copyright?（c）?2002

?屬性????????????大小?????日期????時(shí)間???名稱
-----------?---------??----------?-----??----

?????文件???????2738??2003-02-21?22:39??LS-SVMlab1.5Advanced\AFE.M

?????文件???????5785??2003-02-21?22:39??LS-SVMlab1.5Advanced\bay_errorbar.m

?????文件???????2003??2003-02-21?22:39??LS-SVMlab1.5Advanced\bay_initlssvm.m

?????文件??????10345??2003-02-21?22:39??LS-SVMlab1.5Advanced\bay_lssvm.m

?????文件???????8187??2003-02-21?22:39??LS-SVMlab1.5Advanced\bay_lssvmARD.m

?????文件???????9358??2003-02-21?22:39??LS-SVMlab1.5Advanced\bay_modoutClass.m

?????文件???????5977??2003-02-21?22:39??LS-SVMlab1.5Advanced\bay_optimize.m

?????文件???????4178??2003-02-21?22:39??LS-SVMlab1.5Advanced\BAY_RR.M

?????文件???????5632??2003-02-21?22:39??LS-SVMlab1.5Advanced\changelssvm.m

?????文件???????4245??2003-02-21?22:39??LS-SVMlab1.5Advanced\CODE.M

?????文件???????2118??2003-02-21?22:39??LS-SVMlab1.5Advanced\codedist_bay.m

?????文件????????756??2003-02-21?22:39??LS-SVMlab1.5Advanced\codedist_hamming.m

?????文件???????2018??2003-02-21?22:39??LS-SVMlab1.5Advanced\codedist_loss.m

?????文件???????4125??2003-02-21?22:39??LS-SVMlab1.5Advanced\codelssvm.m

?????文件???????5197??2003-02-21?22:39??LS-SVMlab1.5Advanced\code_ECOC.m

?????文件????????550??2003-02-21?22:39??LS-SVMlab1.5Advanced\code_MOC.m

?????文件????????364??2003-02-21?22:39??LS-SVMlab1.5Advanced\code_OneVsAll.m

?????文件????????555??2003-02-21?22:39??LS-SVMlab1.5Advanced\code_OneVsOne.m

?????文件???????8174??2003-02-21?22:39??LS-SVMlab1.5Advanced\crossvalidate.m

?????文件???????1886??2003-02-21?22:39??LS-SVMlab1.5Advanced\deltablssvm.m

?????文件???????3369??2003-02-21?22:39??LS-SVMlab1.5Advanced\democlass.m

?????文件???????3864??2003-02-21?22:39??LS-SVMlab1.5Advanced\DEMOFUN.M

?????文件???????4747??2003-02-21?22:39??LS-SVMlab1.5Advanced\demomodel.m

?????文件???????2239??2003-02-21?22:39??LS-SVMlab1.5Advanced\demo_fixedclass.m

?????文件???????3099??2003-02-21?22:39??LS-SVMlab1.5Advanced\demo_fixedsize.m

?????文件???????3337??2003-02-21?22:39??LS-SVMlab1.5Advanced\demo_yinyang.m

?????文件???????3507??2003-02-21?22:39??LS-SVMlab1.5Advanced\denoise_kpca.m

?????文件???????3414??2003-02-21?22:39??LS-SVMlab1.5Advanced\EIGN.M

?????文件???????6927??2003-02-21?22:39??LS-SVMlab1.5Advanced\gridsearch.m

?????文件???????4042??2003-02-21?22:39??LS-SVMlab1.5Advanced\initlssvm.m

............此處省略55個(gè)文件信息