代码搜索：One-Class
找到约 293 项符合「One-Class」的源代码
代码结果 293
www.eeworm.com/read/397097/8069117
m gendatout.m
function z = gendatout(a,n,dR)
% z = gendatout(a,n)
%
% Generate outlier objects in a hypersphere round dataset a. This
% dataset should be a one-class dataset. The hypersphere is calculated
% from SV
www.eeworm.com/read/493294/6400237
m dd_ex9.m
% Show the crossvalidation procedure
%
% Generate some simple data, split it in training and testing data using
% 10-fold cross-validation, and compare several one-class classifiers on
% it.
% Copyri
www.eeworm.com/read/493294/6400278
m dlpdda.m
function W = dlpdda(x,nu,usematlab)
%DLPDDA Distance Linear Programming Data Description attracted by the Average distance
%
% W = DLPDDA(D,NU)
%
% This one-class classifier works directly on th
www.eeworm.com/read/492400/6422258
m dd_ex9.m
% Show the crossvalidation procedure
%
% Generate some simple data, split it in training and testing data using
% 10-fold cross-validation, and compare several one-class classifiers on
% it.
% Copyri
www.eeworm.com/read/492400/6422273
m dlpdda.m
function W = dlpdda(x,nu,usematlab)
%DLPDDA Distance Linear Programming Data Description attracted by the Average distance
%
% W = DLPDDA(D,NU)
%
% This one-class classifier works directly on th
www.eeworm.com/read/492400/6422298
m multic.m
%MULTIC Make a multi-class classifier
%
% W = MULTIC(A,V)
%
% Train the (untrained!) one-class classifier V on each of the classes
% in A, and combine it to a multi-class classifier W. If an object
www.eeworm.com/read/400576/11573512
m dd_ex9.m
% Show the crossvalidation procedure
%
% Generate some simple data, split it in training and testing data using
% 10-fold cross-validation, and compare several one-class classifiers on
% it.
% Copyri
www.eeworm.com/read/400576/11573526
m dlpdda.m
function W = dlpdda(x,nu,usematlab)
%DLPDDA Distance Linear Programming Data Description attracted by the Average distance
%
% W = DLPDDA(D,NU)
%
% This one-class classifier works directly on th
www.eeworm.com/read/400576/11573553
m multic.m
%MULTIC Make a multi-class classifier
%
% W = MULTIC(A,V)
%
% Train the (untrained!) one-class classifier V on each of the classes
% in A, and combine it to a multi-class classifier W. If an object
www.eeworm.com/read/213240/15140001
m dd_ex9.m
% Show the crossvalidation procedure
%
% Generate some simple data, split it in training and testing data using
% 10-fold cross-validation, and compare several one-class classifiers on
% it.
% Copyri