代码搜索：classifiers
找到约 2,305 项符合「classifiers」的源代码
代码结果 2,305
www.eeworm.com/read/441245/7673249
m prex_combining.m
%PREX_COMBINING PRTools example on classifier combining
%
% Presents the use of various fixed combiners for some
% classifiers on the 'difficult data'.
%
help prex_combining
echo on
www.eeworm.com/read/397102/8067974
m prex1mod.m
%PREX1 PRTOOLS example of classifiers and scatter plot
help prex1
pause(1)
echo on
A = gendatb(200,200); % Generate banana-shaped classes
% Training set c (40 objects / class)
% Test set d
www.eeworm.com/read/397102/8068498
m prex2.m
%PREX2 PRTOOLS example, plot learning curves of classifiers
help prex2
pause(1)
echo on
% set desired learning sizes
learnsize = [3 5 10 15 20 30];
% Generate Highleyman's classes
A = gend
www.eeworm.com/read/246671/12715231
changelog-3-2-2
2002-02-15 11:35 cvs_mhall
* weka/clusterers/Cobweb.java (stable-3-2-1.2): Changed to reflect
fixes in dev version
2002-02-15 11:20 cvs_mhall
* weka/filters/NominalToBinaryFilter.java (stable
www.eeworm.com/read/143706/12850234
changelog-3-4
2003-11-12 11:39 eibe
* weka/classifiers/meta/Bagging.java (1.25): Changed bagging so
that weights are properly taken into account even if out-of-bag
error is calculated.
2003-11-12 11:07 eibe
www.eeworm.com/read/137160/13341792
m cnormc.m
%CNORMC Classifier normalisation for ML posterior probabilities
%
% W = CNORMC(W,A)
%
% INPUT
% W Classifier mapping
% A Labeled dataset
%
% OUTPUT
% W Scaled classifier mapping
%
% DESCRIPT
www.eeworm.com/read/137160/13342370
m prex_combining.m
%PREX_COMBINING PRTools example on classifier combining
%
% Presents the use of various fixed combiners for some
% classifiers on the 'difficult data'.
%
help prex_combining
echo on
www.eeworm.com/read/136872/13358486
changelog-3-4
2003-11-12 11:39 eibe
* weka/classifiers/meta/Bagging.java (1.25): Changed bagging so
that weights are properly taken into account even if out-of-bag
error is calculated.
2003-11-12 11:07 eibe
www.eeworm.com/read/314653/13562202
m cnormc.m
%CNORMC Classifier normalisation for ML posterior probabilities
%
% W = CNORMC(W,A)
%
% INPUT
% W Classifier mapping
% A Labeled dataset
%
% OUTPUT
% W Scaled classifier mapping
%
% DESCRIPT
www.eeworm.com/read/314653/13562572
m prex_combining.m
%PREX_COMBINING PRTools example on classifier combining
%
% Presents the use of various fixed combiners for some
% classifiers on the 'difficult data'.
%
help prex_combining
echo on