代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
代码结果 4,824
www.eeworm.com/read/314653/13562521
m votec.m
%VOTEC Voting combining classifier
%
% W = VOTEC(V)
% W = V*VOTEC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Voting combiner
%
% DESCRIPTION
% If V = [V1,V2,V3,...] is a stacked set of
www.eeworm.com/read/314653/13562711
m maxc.m
%MAXC Maximum combining classifier
%
% W = MAXC(V)
% W = V*MAXC
%
% INPUT
% V Stacked set of classifiers
%
% OUTPUT
% W Combined classifier using max-rule
%
% DESCRIPTION
% If V = [V1,V2,V
www.eeworm.com/read/493294/6399902
m minc.m
%MINC Minimum combining classifier
%
% W = MINC(V)
% W = V*MINC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Minimum combining classifier on V
%
% DESCRIPTION
% If V = [V1,V2,V3, ...
www.eeworm.com/read/493294/6400004
m plotroc.m
function h = plotroc(e,varargin)
%PLOTROC Draw an ROC curve
%
% H = PLOTROC(W,A)
% H = PLOTROC(E)
%
% Plot the roc curve of E according to the 'traditional' way: on the x
% axis we put the fal
www.eeworm.com/read/493294/6400260
m votec.m
%VOTEC Voting combining classifier
%
% W = VOTEC(V)
% W = V*VOTEC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Voting combiner
%
% DESCRIPTION
% If V = [V1,V2,V3,...] is a stacked set of
www.eeworm.com/read/493294/6400497
m maxc.m
%MAXC Maximum combining classifier
%
% W = MAXC(V)
% W = V*MAXC
%
% INPUT
% V Stacked set of classifiers
%
% OUTPUT
% W Combined classifier using max-rule
%
% DESCRIPTION
% If V = [V1,V2,V
www.eeworm.com/read/492400/6422252
m plotroc.m
function h = plotroc(e,varargin)
%PLOTROC Draw an ROC curve
%
% H = PLOTROC(W,A)
% H = PLOTROC(E)
%
% Plot the roc curve of E according to the 'traditional' way: on the x
% axis we put the fal
www.eeworm.com/read/400577/11572607
m minc.m
%MINC Minimum combining classifier
%
% W = MINC(V)
% W = V*MINC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Minimum combining classifier on V
%
% DESCRIPTION
% If V = [V1,V2,V3, ...
www.eeworm.com/read/400577/11572695
m parzenc.m
%PARZENC Optimisation of the Parzen classifier
%
% [W,H] = PARZENC(A)
% W = PARZENC(A,H,FID)
%
% INPUT
% A dataset
% H smoothing parameter (may be scalar, vector of per-class
% param
www.eeworm.com/read/400577/11572956
m adaboostc.m
%ADABOOSTC
%
% [W,V,ALF] = ADABOOSTC(A,CLASSF,N,RULE,VERBOSE);
%
% INPUT
% A Dataset
% CLASSF Untrained weak classifier
% N Number of classifiers to be trained
% RULE Combinin