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/418695/10935446
m cleval.m
%CLEVAL Classifier evaluation (learning curve)
%
% [e,s] = cleval(classf,A,learnsizes,n,T,print)
%
% Generates at random for all class sizes of the training set
% defined in the vector 'learnsizes
www.eeworm.com/read/418695/10935447
m clevalb.m
%CLEVAL Classifier evaluation (learning curve), bootstrap version
%
% [e,s] = cleval(classf,A,learnsizes,n,T,print)
%
% Generates at random for all class sizes of the training set
% defined in the
www.eeworm.com/read/418695/10935448
m subsc.m
%SUBSC Subspace Classifier
%
% W = subsc(A,n)
%
% n-dimensional subspace maps are computed for each class of the dataset A
% using PCA, such that they contain the origin. All object in A are normalize
www.eeworm.com/read/299984/7140327
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/299984/7140526
m neurc.m
%NEURC Automatic neural network classifier
%
% W = NEURC (A,UNITS)
%
% INPUT
% A Dataset
% UNITS Number of units
% Default: 0.2 x size smallest class in A.
%
% OUTPUT
% W T
www.eeworm.com/read/299984/7140528
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/299984/7140544
m testauc.m
%TESTAUC Multiclass error area under the ROC
%
% E = TESTAUC(A*W)
% E = TESTAUC(A,W)
% E = A*W*TESTAUC
%
% INPUT
% A Dataset to be classified
% W Classifier
%
% OUTPUT
% E Er
www.eeworm.com/read/299984/7140545
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/299984/7140572
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/460435/7250802
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