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/400577/11572606
m qdc.m
%QDC Quadratic Bayes Normal Classifier (Bayes-Normal-2)
%
% [W,R,S,M] = QDC(A,R,S,M)
% W = A*QDC([],R,S)
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0
www.eeworm.com/read/338293/12314529
m contents.m
% Regularization Tools.
% Version 4.1 9-march-08.
% Copyright (c) 1993 and 1998 by Per Christian Hansen and IMM.
%
% Demonstration.
% regudemo - Tutorial introduction to Regularization Tools.
%
%
www.eeworm.com/read/216776/14992883
m olpp.m
function [eigvector, eigvalue, bSuccess] = OLPP(X, W, options)
% OLPP: Orthogonal Locality Preserving Projections
%
% [eigvector, eigvalue, bSuccess] = OLPP(X, W, options)
%
%
www.eeworm.com/read/216775/14992890
m lsda.m
function [eigvector, eigvalue] = LSDA(X, gnd, options)
% LSDA: Locality Sensitive Discriminant Analysis
%
% [eigvector, eigvalue] = LSDA(X, gnd, options)
%
% Input:
%
www.eeworm.com/read/216773/14992899
m lpp.m
function [eigvector, eigvalue] = LPP(X, W, options)
% LPP: Locality Preserving Projections
%
% [eigvector, eigvalue] = LPP(X, W, options)
%
% Input:
% X -
www.eeworm.com/read/213880/15123436
cpp greycstoration4integration.cpp
/*-----------------------------------------------------------------------------
File : greycstoration4integration.cpp
Description : Example of used of the GREYCstoration_4integration
www.eeworm.com/read/210916/15189932
m contents.m
% Regularization Tools.
% Version 3.0 16-April-98.
% Copyright (c) 1993 and 1998 by Per Christian Hansen and IMM.
%
% Demonstration.
% regudemo - Tutorial introduction to Regularization Tools.
%
%
www.eeworm.com/read/200886/15420739
m getsmoothlike.m
% function smoothPriorLikTerm = getSmoothLike(G,z,u)
%
% calculate the regularization part of the log likelihood
% -lambda*sum(diff(trace).^2)
%
% returns one component per class
%
% see also get
www.eeworm.com/read/192667/8367384
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