代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
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www.eeworm.com/read/171622/9744671
readme
/* XCSR_DE1.0
* --------------------------------------------------------
* Learning classifier system based on accuracy in dynamic environments
*
* by Huong Hai (Helen) Dam
* z3140959@itee.
www.eeworm.com/read/431675/8662250
m classim.m
%CLASSIM Classify image using a given classifier
%
% labels = classim(D,N)
%
% Returns an image with the labels of the classified datasetimage D
% (typically the result of a mapping or classificat
www.eeworm.com/read/386050/8768320
m subsc.m
%SUBSC Subspace Classifier
%
% W = SUBSC(A,N)
% W = SUBSC(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Desired model dimensionality or fraction of retained
% variance per
www.eeworm.com/read/280531/10317916
m showobjectmodel.m
% This script uses the parameters of the boosted detector and visualizes
% a part based model of the object by ploting the features used by the
% classifier.
clear all
parameters
% Load d
www.eeworm.com/read/279339/10446015
m showobjectmodel.m
% This script uses the parameters of the boosted detector and visualizes
% a part based model of the object by ploting the features used by the
% classifier.
clear all
parameters
% Load d
www.eeworm.com/read/418695/10935576
m classim.m
%CLASSIM Classify image using a given classifier
%
% labels = classim(D,N)
%
% Returns an image with the labels of the classified datasetimage D
% (typically the result of a mapping or classificat
www.eeworm.com/read/299984/7140361
m subsc.m
%SUBSC Subspace Classifier
%
% W = SUBSC(A,N)
% W = SUBSC(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Desired model dimensionality or fraction of retained
% variance per
www.eeworm.com/read/460435/7250836
m subsc.m
%SUBSC Subspace Classifier
%
% W = SUBSC(A,N)
% W = SUBSC(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Desired model dimensionality or fraction of retained
% variance per
www.eeworm.com/read/455917/7361838
m ldaclassifier.m
function k=LDAClassifier(n,m,classcenter,x)
% LDACLASSIFIER is the implementation of the classifier based on LDA
% n denotes the dimension of the problem
% m is the number of classes
% classcenter
www.eeworm.com/read/455917/7361839
m pcaclassifier.m
function k=PCAClassifier(n,m,classcenter,x)
% PCACLASSIFIER is the implementation of the classifier based on PCA
% n denotes the dimension of the problem
% m is the number of classes
% classcenter