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/168045/9941054
m rbf.m
function w = rbf(x, t, d, sigma, lam)
% function w = rbf(x,t,d,sigma,lam)
%
% Determines weights for a regularized radial basis function network.
%
% x - data
% t - centers
% d - de
www.eeworm.com/read/360895/10072672
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/161189/10439635
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.
www.eeworm.com/read/278889/10490530
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/421949/10676064
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/418911/10891995
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/299984/7140689
m linearr.m
%LINEARR Linear regression
%
% Y = LINEARR(X,LAMBDA,N)
%
% INPUT
% X Dataset
% LAMBDA Regularization parameter (default: no regularization)
% N Order of polynomial (optional)
%
www.eeworm.com/read/460435/7251165
m linearr.m
%LINEARR Linear regression
%
% Y = LINEARR(X,LAMBDA,N)
%
% INPUT
% X Dataset
% LAMBDA Regularization parameter (default: no regularization)
% N Order of polynomial (optional)
%
www.eeworm.com/read/441245/7673385
m linearr.m
%LINEARR Linear regression
%
% Y = LINEARR(X,LAMBDA,N)
%
% INPUT
% X Dataset
% LAMBDA Regularization parameter (default: no regularization)
% N Order of polynomial (optional)
%
www.eeworm.com/read/397122/8065814
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