代码搜索：APPROXIMATION
找到约 1,542 项符合「APPROXIMATION」的源代码
代码结果 1,542
www.eeworm.com/read/314681/13561814
m fdet_chebyshev2.m
function [detbounds]=fdet_chebyshev2(d, alphafine)
%
%[detbounds]=fdet_chebyshev2(d, alphafine)
%
%This function returns a quadratic lower Taylor series bound, a quadratic Chebyshev approximation,
www.eeworm.com/read/344512/11876238
m stls.m
% STLS - Structured Total Least Squares approximation.
%
% Solves the structured system of equations A*X = B with size(A,1) > size(A,2).
% The augmented data matrix C = [A B] is of the form C = [C1 ..
www.eeworm.com/read/188324/8550670
m pckkntdm.m
%% How to choose knots when you have to
%
% Illustration of the use of OPTKNT and NEWKNT.
% Copyright 1987-2005 C. de Boor and The MathWorks, Inc.
% $Revision: 1.18.4.2 $
%% Sample fu
www.eeworm.com/read/188324/8550711
m tspdem.m
%% The Tensor Product Construct
%
% Illustrate approximation to data on rectangular grid.
% Copyright 1987-2005 C. de Boor and The MathWorks, Inc.
% $Revision: 1.18.4.2 $
%%
% Since the
www.eeworm.com/read/354578/10344851
m comb_coars_disj.m
function [m,Fv,F,C]=comb_coars_disj(mm,FF,K,version);
% Copyright Thierry Denoeux
% April 4, 2002
%
% Approximate disjunctive combination of several basic belief assignments (bba's),
% using
www.eeworm.com/read/354578/10344855
m comb_coars.m
function [m,Fv,F,C]=comb_coars(mm,FF,K,version);
% Copyright Thierry Denoeux
% April 4, 2002
%
% Approximate conjunctive combination of several basic belief assignments (bba's),
% using the
www.eeworm.com/read/354578/10344864
m appcoars_m.m
function [mm,FFc,FFa,C,N,N0]=appcoars_m(mm,FF,K,version);
% Copyright Thierry Denoeux
% April 4, 2002
%
% Joint inner and outer coarsening approximation of several
% basic belief assignmen
www.eeworm.com/read/349111/10848941
tex wtls_manual.tex
\documentclass[10pt]{article}
%-------------------------------------------------------------
\usepackage{graphicx}
\usepackage{psfrag}
\usepackage{amsmath}
\usepackage{amsthm}
\usepackage{amsfonts}
\u
www.eeworm.com/read/469123/6977814
m contents.m
% gpml: code from Rasmussen & Williams: Gaussian Processes for Machine Learning
% date: 2007-07-25.
%
% approxEP.m - the approximation method for Expectation Propagation
% approxLA.m -