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/190459/8443073
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/190387/8444270
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/289321/8559328
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/289321/8559349
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
www.eeworm.com/read/386050/8769454
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/429504/8804801
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/428451/8867225
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/427586/8932002
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/183445/9158681
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/374698/9388859
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