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/441245/7673256
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/297150/8050481
bat weka_decision_tree.bat
java weka.classifiers.j48.J48 -t training_data.arff -x 2
www.eeworm.com/read/397102/8068538
m maxc.m
%MAXC Maximum combining classifier
%
% W = maxc(V)
% W = V*maxc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the maximum combiner: it selects the cla
www.eeworm.com/read/143706/12850302
readme_knowledgeflow
===============================================================
KnowledgeFlow GUI Quick Primer
===============================================================
The KnowledgeFlow provides an alternativ
www.eeworm.com/read/137160/13341790
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/137160/13341860
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/137160/13341917
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/137160/13342273
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/137160/13342291
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/137160/13342295
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