代码搜索：Variance
找到约 2,271 项符合「Variance」的源代码
代码结果 2,271
www.eeworm.com/read/137160/13341920
m pcldc.m
%PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (defau
www.eeworm.com/read/314653/13562266
m pcldc.m
%PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (defau
www.eeworm.com/read/307764/6342522
m montecarlo.m
%this program is a monte carlo computer simulation that was
%used to generate figure 2.10a
%set seed of random number generator to initial value
randn('seed',0);
%set up values of variance, data
www.eeworm.com/read/493294/6399985
m pcldc.m
%PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (defau
www.eeworm.com/read/493294/6400539
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright:
www.eeworm.com/read/492400/6422326
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright:
www.eeworm.com/read/485902/6549014
m rlsi.m
%
% Modeling (Standard RLS Algorithm I - Table 12.1)
%
%
% Last updated on April 28, 1998
%
itn=input('\n No. of iterations? ');
sigman2=input('\n Variance of the plant noise? ');
www.eeworm.com/read/400577/11572675
m pcldc.m
%PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (defau
www.eeworm.com/read/400576/11573585
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright:
www.eeworm.com/read/255755/12057363
m pcldc.m
%PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (defau