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/314653/13562559
m clevals.m
%CLEVALS Classifier evaluation (feature size/learning curve), bootstrap possible
%
% E = CLEVALS(A,CLASSF,FEATSIZE,TRAINSIZES,NREPS,T,FID)
%
% INPUT
% A Training dataset
% CLASSF Cl
www.eeworm.com/read/314653/13562577
m reject.m
%REJECT Compute the error-reject trade-off curve
%
% E = REJECT(D);
% E = REJECT(A,W);
%
% INPUT
% D Classification result, D = A*W
% A Dataset
% W Cell array of trained classifiers
www.eeworm.com/read/493294/6399866
m medianc.m
%MEDIANC Median combining classifier
%
% W = MEDIANC(V)
% W = V*MEDIANC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Median combining classifier on V
%
% DESCRIPTION
% If V = [V
www.eeworm.com/read/493294/6399933
m averagec.m
%AVERAGEC Combining of linear classifiers by averaging coefficients
%
% W = AVERAGEC(V)
% W = V*AVERAGEC
%
% INPUT
% V A set of affine base classifiers.
%
% OUTPUT
% W Combined classifier.
%
%
www.eeworm.com/read/493294/6399980
m prodc.m
%PRODC Product combining classifier
%
% W = PRODC(V)
% W = V*PRODC
%
% INPUT
% V Set of classifiers trained on the same classes
%
% OUTPUT
% W Product combiner
%
% DESCRIPTION
% It def
www.eeworm.com/read/493294/6400237
m dd_ex9.m
% Show the crossvalidation procedure
%
% Generate some simple data, split it in training and testing data using
% 10-fold cross-validation, and compare several one-class classifiers on
% it.
% Copyri
www.eeworm.com/read/493294/6400261
m meanc.m
%MEANC Mean combining classifier
%
% W = MEANC(V)
% W = V*MEANC
%
% INPUT
% V Set of classifiers (optional)
%
% OUTPUT
% W Mean combiner
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a s
www.eeworm.com/read/493294/6400280
m cleval.m
%CLEVAL Classifier evaluation (learning curve)
%
% E = CLEVAL(A,CLASSF,TRAINSIZES,NREPS,T,FID)
%
% INPUT
% A Training dataset
% CLASSF Classifier to evaluate
% TRAINSIZES Vecto
www.eeworm.com/read/493294/6400285
m clevalb.m
%CLEVALB Classifier evaluation (learning curve), bootstrap version
%
% E = CLEVALB(A,CLASSF,TRAINSIZES,N,FID)
%
% INPUT
% A Training dataset
% CLASSF Classifier to evaluate
% TR
www.eeworm.com/read/493294/6400308
m stacked.m
%STACKED Combining classifiers in the same feature space
%
% WC = STACKED(W1,W2,W3, ....) or WC = [W1,W2,W3, ...]
% WC = STACKED({W1,W2,W3, ...}) or WC = [{W1,W2,W3, ...}]
% WC = STACKED(WC,W1,