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/353769/10418548
c mcnemar.c
/************************************************************************
* *
* Program package 'lvq_pak':
www.eeworm.com/read/161374/10421266
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/418695/10935598
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/299984/7139933
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/299984/7140555
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/460435/7250408
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/460435/7251031
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/450608/7480074
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/450608/7480444
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/441245/7672610
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