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/137160/13342256
m nbayesc.m
%NBAYESC Bayes Classifier for given normal densities
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% W = NBAYESC(U,G)
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% INPUT
% U Dataset of means of classes
% G Covariance matrices (optional; default: identity matrices)
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% OUTP
www.eeworm.com/read/137160/13342329
m neurc.m
%NEURC Automatic neural network classifier
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% W = NEURC (A,UNITS)
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% INPUT
% A Dataset
% UNITS Array indicating number of units in each hidden layer (default: [5])
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% OUTPUT
% W Tra
www.eeworm.com/read/137160/13342332
m testp.m
%TESTP Error estimation of Parzen classifier
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% E = TESTP(A,H,T)
% E = TESTP(A,H)
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% INPUT
% A input dataset
% H matrix smoothing parameters (optional, def: determined via
%
www.eeworm.com/read/137160/13342353
m bayesc.m
%BAYESC Bayes classifier
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% W = BAYESC(WA,WB, ... ,P,LABLIST)
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% INPUT
% WA, WB, ... Trained mappings for supplying class density estimates
% P Vector with class prior probabili
www.eeworm.com/read/137160/13342391
m getcost.m
%GETCOST Get classification cost matrix
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% [COST,LABLIST] = GETCOST(W)
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% Returns the classification cost matrix as set in the classifier W.
% An empty cost matrix is interpreted as equal costs for
www.eeworm.com/read/320830/13417570
m average_precision.m
function Average_Precision=Average_precision(Outputs,test_target)
%Computing the average precision
%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class
www.eeworm.com/read/320830/13417572
m ranking_loss.m
function RankingLoss=Ranking_loss(Outputs,test_target)
%Computing the hamming loss
%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class is stored in Ou
www.eeworm.com/read/314653/13562513
m nbayesc.m
%NBAYESC Bayes Classifier for given normal densities
%
% W = NBAYESC(U,G)
%
% INPUT
% U Dataset of means of classes
% G Covariance matrices (optional; default: identity matrices)
%
% OUTP
www.eeworm.com/read/314653/13562552
m neurc.m
%NEURC Automatic neural network classifier
%
% W = NEURC (A,UNITS)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each hidden layer (default: [5])
%
% OUTPUT
% W Tra
www.eeworm.com/read/314653/13562553
m testp.m
%TESTP Error estimation of Parzen classifier
%
% E = TESTP(A,H,T)
% E = TESTP(A,H)
%
% INPUT
% A input dataset
% H matrix smoothing parameters (optional, def: determined via
%