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/351797/10609635
m display.m
function display(net)
% DISPLAY
%
% Display a textual representation of a support vector classifier object.
%
% display(net);
%
% File : @svc/display.m
%
% Date : Wednesd
www.eeworm.com/read/349842/10796736
m contents.m
% Classification GUI and toolbox
% Version 1.0
%
% GUI start commands
%
% classifier - Start the classification GUI
% enter_distributions - Starts the parameter input screen (used by classif
www.eeworm.com/read/418695/10935174
m klclc.m
%KLCLC Linear classifier using KL expansion of common covariance
% matrix
%
% W = klclc(A,n)
%
% Finds the linear discriminant function W for the dataset A
% computing the ldc on a projection of
www.eeworm.com/read/418695/10935431
m nmsc.m
%NMSC Nearest Mean Scaled Classifier
%
% W = nmsc(A)
%
% Computation of the linear discriminant for the classes in the
% dataset A assuming zero covariances and equal class variances.
%
% See als
www.eeworm.com/read/418695/10935452
m ldc.m
%LDC Linear Discriminant Classifier
%
% W = ldc(A,r,s)
%
% Computation of a linear discriminant between the classes of the
% dataset A assuming normal densities with equal covariance
% matrices.
www.eeworm.com/read/299984/7140702
m fixedcc.m
%FIXEDCC Construction of fixed combiners
%
% V = FIXEDCC(A,W,TYPE,NAME)
%
% INPUT
% A Dataset
% W A set of classifier mappings
% TYPE The type of combination rule
% NAME The na
www.eeworm.com/read/460435/7251178
m fixedcc.m
%FIXEDCC Construction of fixed combiners
%
% V = FIXEDCC(A,W,TYPE,NAME)
%
% INPUT
% A Dataset
% W A set of classifier mappings
% TYPE The type of combination rule
% NAME The na
www.eeworm.com/read/455917/7361840
m mindisclassifier.m
function k=MinDisClassifier(n,m,classcenter,x)
% MINDISCLASSIFIER is the implementation of minimum-distance classifier
% n denotes the dimension of the problem
% m is the number of classes
% class
www.eeworm.com/read/450608/7480572
m fixedcc.m
%FIXEDCC Construction of fixed combiners
%
% V = FIXEDCC(A,W,TYPE,NAME)
%
% INPUT
% A Dataset
% W A set of classifier mappings
% TYPE The type of combination rule
% NAME The na
www.eeworm.com/read/441245/7673231
m clevals.m
%CLEVALS Classifier evaluation (feature size/learning curve), bootstrap possible
%
% E = CLEVALS(A,CLASSF,FEATSIZE,TRAINSIZES,NREPS,T)
%
% INPUT
% A Training dataset
% CLASSF Classi