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/150749/12267202
m leaveoneout_lssvm.m
function [costs, z, yh, model] = leaveoneout_lssvm(model,gams, estfct)
% Fast leave-one-out cross-validation for the LS-SVM based on one full matrix inversion
%
% >> cost = leaveoneout_lssvm({X,Y,typ
www.eeworm.com/read/150749/12267330
m leaveoneout_lssvm.m
function [costs, z, yh, model] = leaveoneout_lssvm(model,gams, estfct)
% Fast leave-one-out cross-validation for the LS-SVM based on one full matrix inversion
%
% >> cost = leaveoneout_lssvm({X,Y,typ
www.eeworm.com/read/338293/12314624
m regutm.m
function [A,U,V] = regutm(m,n,s)
%REGUTM Test matrix for regularization methods.
%
% [A,U,V] = regutm(m,n,s)
%
% Generates a random m-times-n matrix A such that A*A' and A'*A
% are oscillating. Hence
www.eeworm.com/read/234163/14120257
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/119681/14824450
m leaveoneout_lssvm.m
function [costs, z, yh, model] = leaveoneout_lssvm(model,gams, estfct)
% Fast leave-one-out cross-validation for the LS-SVM based on one full matrix inversion
%
% >> cost = leaveoneout_lssvm({X,Y,typ
www.eeworm.com/read/216771/14992910
m lda.m
function [eigvector, eigvalue] = LDA(X,gnd,options)
% LDA: Linear Discriminant Analysis
%
% [eigvector, eigvalue] = LDA(X, gnd, options)
%
% Input:
% X -
www.eeworm.com/read/214923/15082941
m leaveoneout_lssvm.m
function [costs, z, yh, model] = leaveoneout_lssvm(model,gams, estfct)
% Fast leave-one-out cross-validation for the LS-SVM based on one full matrix inversion
%
% >> cost = leaveoneout_lssvm({X,Y,typ
www.eeworm.com/read/210916/15189954
m regutm.m
function [A,U,V] = regutm(m,n,s)
%REGUTM Test matrix for regularization methods.
%
% [A,U,V] = regutm(m,n,s)
%
% Generates a random m-times-n matrix A such that A*A' and A'*A
% are oscillating. Hence
www.eeworm.com/read/471135/6898149
m iridge.m
function [B, S, h, peff] = iridge(Cxx, Cyy, Cxy, dof, options);
%IRIDGE Individual ridge regressions with generalized cross-validation.
%
% [B, S, h, peff] = IRIDGE(Cxx, Cyy, Cxy, dof) returns a re
www.eeworm.com/read/471135/6898153
m mridge.m
function [B, S, h, peff] = mridge(Cxx, Cyy, Cxy, dof, options);
%MRIDGE Multiple ridge regression with generalized cross-validation.
%
% [B, S, h, peff] = MRIDGE(Cxx, Cyy, Cxy, dof, OPTIONS) return