代码搜索：Approximation
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www.eeworm.com/read/140697/13066842
m alg101.m
% NEWTON'S METHOD FOR SYSTEMS ALGORITHM 10.1
%
% To approximate the solution of the nonlinear system F(X)=0 given
% an initial approximation X:
%
% INPUT: Number n of equations and unknowns; in
www.eeworm.com/read/140697/13067040
m alg101.m
% NEWTON'S METHOD FOR SYSTEMS ALGORITHM 10.1
%
% To approximate the solution of the nonlinear system F(X)=0 given
% an initial approximation X:
%
% INPUT: Number n of equations and unknowns; in
www.eeworm.com/read/215323/15065133
tm divonne.tm
:Evaluate: BeginPackage["Cuba`"]
:Evaluate: Divonne::usage =
"Divonne[f, {x, xmin, xmax}..] computes a numerical approximation to the integral of the real scalar or vector function f.
The output is
www.eeworm.com/read/457216/1599701
m contents.m
% Chapter 6: Approximation and fitting
%
% deadzone.m - Section 6.1.2: Residual minimization with deadzone penalty
% fig6_15.m - Figure 6.15: A comparison of stochastic and
www.eeworm.com/read/435228/1865387
asm cos.asm
;
; Project: Experiment 3.6.6.5 Real Time Signal Generation - Chapter 3
; File name: cos.asm
;
; Description: 16-bit cos(x) approximation func
www.eeworm.com/read/435228/1865413
asm cos.asm
;
; Project: Experiment 3.6.6.2 Real Time Signal Generation - Chapter 3
;
; File name: cos.asm
;
; Description: 16-bit cos(x) approximation f
www.eeworm.com/read/409299/2234962
svn-base dualgeneralfeaturesapprox.m.svn-base
function [testInfo, projectionInfo] = dualGeneralFeaturesApprox(trainData, testData, subspaceInfo, params)
%Compute kernel matrix approximation for dual general features using
%\tilde{K} = K - K_{j
www.eeworm.com/read/373026/2767637
m contents.m
% Chapter 6: Approximation and fitting
%
% deadzone.m - Section 6.1.2: Residual minimization with deadzone penalty
% fig6_15.m - Figure 6.15: A comparison of stochastic and
www.eeworm.com/read/287267/8699037
m mregwav2.m
% Example of multiscale approximation using
% Regularization Networks
%
% Sin/Sinc frame are used for approximating a
% Sin + sinc functions.
%
%
% 30/10/2000 AR
%
%
close all
clear all
www.eeworm.com/read/386253/8759942
m alg103.m
% STEEPEST DESCENT ALGORITHM 10.3
%
% To approximate a solution P to the minimization problem
% G(P) = MIN( G(X) : X in R(n) )
% given an initial approximation X:
%
% INPUT: Num