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/460435/7250836
m subsc.m
%SUBSC Subspace Classifier
%
% W = SUBSC(A,N)
% W = SUBSC(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Desired model dimensionality or fraction of retained
% variance per
www.eeworm.com/read/450608/7480152
m gendatb.m
%GENDATB Generation of banana shaped classes
%
% A = GENDATB(N,S)
%
% INPUT
% N number of generated samples of vector with
% number of samples per class
% S variance
www.eeworm.com/read/450608/7480411
m subsc.m
%SUBSC Subspace Classifier
%
% W = SUBSC(A,N)
% W = SUBSC(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Desired model dimensionality or fraction of retained
% variance per
www.eeworm.com/read/441245/7672733
m gendatb.m
%GENDATB Generation of banana shaped classes
%
% A = GENDATB(N,S)
%
% INPUT
% N number of generated samples of vector with
% number of samples per class
% S variance
www.eeworm.com/read/441245/7673050
m subsc.m
%SUBSC Subspace Classifier
%
% W = SUBSC(A,N)
% W = SUBSC(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Desired model dimensionality or fraction of retained
% variance per
www.eeworm.com/read/137160/13341937
m gendatb.m
%GENDATB Generation of banana shaped classes
%
% A = GENDATB(N,S)
%
% INPUT
% N number of generated samples of vector with
% number of samples per class
% S variance
www.eeworm.com/read/137160/13342298
m subsc.m
%SUBSC Subspace Classifier
%
% W = SUBSC(A,N)
% W = SUBSC(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Desired model dimensionality or fraction of retained
% variance per
www.eeworm.com/read/319478/13450958
res agreepv.res
OBSERVED VALUE OF DELTA = 3.5039063
EXPECTED VALUE OF DELTA = 4.2975098
VARIANCE OF DELTA = 0.93170749E-01
SKEWNESS OF DELTA = -0.13898623
STANDARDIZED VALUE O
www.eeworm.com/read/314653/13562280
m gendatb.m
%GENDATB Generation of banana shaped classes
%
% A = GENDATB(N,S)
%
% INPUT
% N number of generated samples of vector with
% number of samples per class
% S variance
www.eeworm.com/read/314653/13562539
m subsc.m
%SUBSC Subspace Classifier
%
% W = SUBSC(A,N)
% W = SUBSC(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Desired model dimensionality or fraction of retained
% variance per