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/255755/12058057
m getcost.m
%GETCOST Get classification cost matrix
%
% [COST,LABLIST] = GETCOST(W)
%
% 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/150905/12249128
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/150905/12249277
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/150905/12249283
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
%
www.eeworm.com/read/150905/12249324
m bayesc.m
%BAYESC Bayes classifier
%
% W = BAYESC(WA,WB, ... ,P,LABLIST)
%
% INPUT
% WA, WB, ... Trained mappings for supplying class density estimates
% P Vector with class prior probabili
www.eeworm.com/read/150905/12249359
m getcost.m
%GETCOST Get classification cost matrix
%
% [COST,LABLIST] = GETCOST(W)
%
% 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/150760/12266067
m~ train_ocr.m~
% TRAIN_OCR Training of OCR classifier based on multiclass SVM.
%
% Description:
% The following steps are performed:
% - Training set is created from data in directory ExamplesDir.
% - Mult
www.eeworm.com/read/150760/12266074
m train_ocr.m
% TRAIN_OCR Training of OCR classifier based on multiclass SVM.
%
% Description:
% The following steps are performed:
% - Training set is created from data in directory ExamplesDir.
% - Mult
www.eeworm.com/read/149739/12353491
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/149739/12353570
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