代码搜索：Regularization
找到约 355 项符合「Regularization」的源代码
代码结果 355
www.eeworm.com/read/411401/11246869
m makeregmatrix.m
function R=MakeRegmatrix(Element);
%MakeRegmatrix Computes a regularisation matrix which includes smoothness assumptions
% Function R=MakeRegmatrix(Element);
% computes a regularization matrix R whic
www.eeworm.com/read/386050/8767368
m ridger.m
%RIDGER Ridge Regression
%
% W = RIDGER(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter (default LAMBDA=1)
%
% OUTPUT
% W Ridge regression mappin
www.eeworm.com/read/360995/10070145
m svddpath.m
%SVDDPATH SVDD for different lambda/C
%
% W = SVDDPATH(A,FRACREJ,KTYPE,KPAR)
%
% Optimize the SVDD over the complete regularization path by changing C
% (or lambda). The SVDD is defined by th
www.eeworm.com/read/299984/7139962
m ridger.m
%RIDGER Ridge Regression
%
% W = RIDGER(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter (default LAMBDA=1)
%
% OUTPUT
% W Ridge regression mappin
www.eeworm.com/read/460435/7250437
m ridger.m
%RIDGER Ridge Regression
%
% W = RIDGER(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter (default LAMBDA=1)
%
% OUTPUT
% W Ridge regression mappin
www.eeworm.com/read/451547/7461998
m svddpath.m
%SVDDPATH SVDD for different lambda/C
%
% W = SVDDPATH(A,FRACREJ,KTYPE,KPAR)
%
% Optimize the SVDD over the complete regularization path by changing C
% (or lambda). The SVDD is defined by th
www.eeworm.com/read/441245/7672641
m ridger.m
%RIDGER Ridge Regression
%
% W = RIDGER(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter (default LAMBDA=1)
%
% OUTPUT
% W Ridge regression mappin
www.eeworm.com/read/493294/6400505
m svddpath.m
%SVDDPATH SVDD for different lambda/C
%
% W = SVDDPATH(A,FRACREJ,KTYPE,KPAR)
%
% Optimize the SVDD over the complete regularization path by changing C
% (or lambda). The SVDD is defined by th
www.eeworm.com/read/492400/6422316
m svddpath.m
%SVDDPATH SVDD for different lambda/C
%
% W = SVDDPATH(A,FRACREJ,KTYPE,KPAR)
%
% Optimize the SVDD over the complete regularization path by changing C
% (or lambda). The SVDD is defined by th
www.eeworm.com/read/400577/11572605
m ridger.m
%RIDGER Ridge Regression
%
% W = RIDGER(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter (default LAMBDA=1)
%
% OUTPUT
% W Ridge regression mappin