代码搜索：Stepwise
找到约 97 项符合「Stepwise」的源代码
代码结果 97
www.eeworm.com/read/472943/1402622
m gendatafig7.m
% Generates data to test Stepwise phase transition with noise
clear all;
p=500; n=250;
zi=1;
ErrVecL2n = RunSimulation0(n,p,zi,'Stepwise2logp');
save Fig7Datazi0 ErrVecL2n p n zi;
zi=1;
ErrVecL2n = R
www.eeworm.com/read/472943/1402625
m gendatafig6.m
% Generates data to test Stepwise phase transition with noise
clear all;
p=200; n=100;
zi=1;
ErrVecL2n = RunSimulation0(n,p,zi,'Stepwise2logp');
save Fig6Datazi0 ErrVecL2n p n zi;
zi=1;
ErrVecL2n = R
www.eeworm.com/read/152068/12146654
lst 例15-03计算结果.lst
The SAS System 21:18 Tuesday, April 2, 2002 4
Stepwise Procedure for Dependent Variable Y
Step 1 Variable X4 Entered
www.eeworm.com/read/472943/1402617
m msnvenofig5.m
% Figure 5: This phase diagram shows the implementation of the Forward
% Stepwise Algorithm, but with a False Discovery Rate threshold: a
% term is added to the model if it has the largest t-statis
www.eeworm.com/read/472943/1402601
m msnvenofig4.m
% Figure 4: Phase diagram when the underlying sparse model is recovered
% using the Forward Stepwise Algorithm, with the number of variables, p,
% fixed at 200. Variables were greedily added to the
www.eeworm.com/read/472943/1402608
m msnvenofig6.m
% Figure 6: This shows a single vertical slice of the Phase Diagram
% for Forward Stepwise (Fig 4), with varying noise levels, with
% delta=n/p fixed at .5 and the number of variables fixed at 200
www.eeworm.com/read/472943/1402607
m msnvenofig7.m
% Figure 7: This shows vertical slices at delta=n/p=.5 through the
% Forward Stepwise Phase Diagram (Fig 4), with the number of variables
% now fixed at 500, and the number of replications at 300. A
www.eeworm.com/read/472943/1402618
m msnvenofig11.m
% Figure 11: Ratio of median Forward Stepwise MSE to the median oracle MSE.
% The number of variables is fixed at 500, the number of observations at 250,
% maintaining delta=n/p=.5, and the median w
www.eeworm.com/read/191902/8417356
m sohc.m
function [features, targets, label] = SOHC(train_features, train_targets, Nmu, region, plot_on)
%Reduce the number of data points using the stepwise optimal hierarchical clustering algorithm
%Inpu
www.eeworm.com/read/286662/8751955
m sohc.m
function [patterns, targets, label] = SOHC(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the stepwise optimal hierarchical clustering algorithm
%Inputs:
% t