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/137213/13339973
m confusion_matrix.m
function CM = Confusion_matrix(train_predicts, train_targets)
% solve the confusion matrix of classifiers
% Inputs:
% predicts - the predicting class by single classifiers
% targets
www.eeworm.com/read/137160/13342326
m plotd.m
%PLOTD Plot classifiers, outdated, use PLOTC instead
% $Id: plotd.m,v 1.4 2003/12/14 22:13:21 bob Exp $
function handle = plotd(varargin)
prtrace(mfilename);
global PLOTD_REPLACED_BY_PLOTC
if
www.eeworm.com/read/136872/13358508
changelog-3-3-2
2002-05-21 14:35 eibe
* weka/classifiers/rules/JRip.java (1.4): Minor bug fix in JRip
regarding data description length for default rule.
2002-05-15 09:35 mhall
* weka/attributeSelection/SVMAt
www.eeworm.com/read/314653/13562551
m plotd.m
%PLOTD Plot classifiers, outdated, use PLOTC instead
% $Id: plotd.m,v 1.4 2003/12/14 22:13:21 bob Exp $
function handle = plotd(varargin)
prtrace(mfilename);
global PLOTD_REPLACED_BY_PLOTC
if
www.eeworm.com/read/312163/13617563
m~ contents.m~
% Algorithms learning linear classifiers from finite vector sets.
%
% ekozinec - Kozinec's algorithm for eps-optimal separating hyperplane.
% ekozinec2 - Kozinec's algorithm for eps-optimal separ
www.eeworm.com/read/134901/5891552
m~ contents.m~
% Algorithms learning linear classifiers from finite vector sets.
%
% ekozinec - Kozinec's algorithm for eps-optimal separating hyperplane.
% ekozinec2 - Kozinec's algorithm for eps-optimal separ
www.eeworm.com/read/493294/6400267
m dd_ex4.m
% DD_EX4
%
% This should show the use of consistent_occ, for the optimization
% of complexity parameters of one-class classifiers. This function
% can be applied to all one-class classifiers in the to
www.eeworm.com/read/493294/6400304
m plotd.m
%PLOTD Plot classifiers, outdated, use PLOTC instead
% $Id: plotd.m,v 1.4 2003/12/14 22:13:21 bob Exp $
function handle = plotd(varargin)
prtrace(mfilename);
global PLOTD_REPLACED_BY_PLOTC
if
www.eeworm.com/read/493294/6400452
m multic.m
%MULTIC Make a multi-class classifier
%
% W = MULTIC(A,V)
%
% Train the (untrained!) one-class classifier V on each of the classes
% in A, and combine it to a multi-class classifier W. If an object
www.eeworm.com/read/493294/6400470
m dd_ex7.m
% Show how several one-class classifiers can be combined.
% To make the classifier outputs comparable, the outputs should be
% normalized using dd_normc
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org