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/460435/7251001
m neurc.m
%NEURC Automatic neural network classifier
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% W = NEURC (A,UNITS)
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% INPUT
% A Dataset
% UNITS Number of units
% Default: 0.2 x size smallest class in A.
%
% OUTPUT
% W T
www.eeworm.com/read/460435/7251003
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/460435/7251020
m testauc.m
%TESTAUC Multiclass error area under the ROC
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% E = TESTAUC(A*W)
% E = TESTAUC(A,W)
% E = A*W*TESTAUC
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% INPUT
% A Dataset to be classified
% W Classifier
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% OUTPUT
% E Er
www.eeworm.com/read/460435/7251021
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/460435/7251048
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/451547/7461977
m incsvdd.m
%INCSVDD Incremental Support Vector Classifier
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% W = INCSVDD(A,FRACERR,KTYPE,PAR)
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% Use the incremental version of the SVDD. The kernel is defined by
% KTYPE, with the free parameter PAR. See
www.eeworm.com/read/450608/7480385
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/450608/7480424
m neurc.m
%NEURC Automatic neural network classifier
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% 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/450608/7480425
m testp.m
%TESTP Error estimation of Parzen classifier
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% E = TESTP(A,H,T)
% E = TESTP(A,H)
%
% INPUT
% A input dataset
% H matrix smoothing parameters (optional, def: determined via
%