代码搜索：SVDD
找到约 94 项符合「SVDD」的源代码
代码结果 94
www.eeworm.com/read/427378/8948959
m svdd.m
clc
close all
nu =0.6; % nu -> [0,1] 在支持向量数与错分样本数之间进行折衷
% 支持向量机的 nu 参数(取值越小，异常点就越少)
%ker = struct('type','linear');
ker = struct('type','gauss','width',0.45);
%
www.eeworm.com/read/427378/8948972
asv svdd.asv
clc
close all
nu =0.05; % nu -> [0,1] 在支持向量数与错分样本数之间进行折衷
% 支持向量机的 nu 参数(取值越小，异常点就越少)
%ker = struct('type','linear');
ker = struct('type','gauss','width',4.5);
%
www.eeworm.com/read/360995/10069929
m svdd.m
%SVDD Support Vector Data Description
%
% W = SVDD(A,FRACREJ,SIGMA)
%
% Optimizes a support vector data description for the dataset A by
% quadratic programming. The data description use
www.eeworm.com/read/451547/7461923
m svdd.m
%SVDD Support Vector Data Description
%
% W = SVDD(A,FRACREJ,SIGMA)
%
% Optimizes a support vector data description for the dataset A by
% quadratic programming. The data description use
www.eeworm.com/read/397111/8067174
m svdd.m
%SVDD Support Vector Data Description
%
% W = SVDD(A,FRACREJ,SIGMA)
%
% Optimizes a support vector data description for the dataset A by
% quadratic programming. The data description use
www.eeworm.com/read/397097/8069129
m svdd.m
%SVDD Support Vector Data Description
%
% [W,out,J] = svdd(A,fracrej,fracerr)
%
% Optimizes a support vector data description for the dataset A by
% quadratic programming. The data descr
www.eeworm.com/read/493294/6399978
m svdd.m
%SVDD Support Vector Data Description
%
% W = SVDD(A,FRACREJ,SIGMA)
%
% Optimizes a support vector data description for the dataset A by
% quadratic programming. The data description use
www.eeworm.com/read/492400/6422242
m svdd.m
%SVDD Support Vector Data Description
%
% W = SVDD(A,FRACREJ,SIGMA)
%
% Optimizes a support vector data description for the dataset A by
% quadratic programming. The data description use
www.eeworm.com/read/483910/6597434
pdf svdd.pdf
www.eeworm.com/read/482887/6615693
m svdd.m
function model = svdd(X,options)
% SVDD Minimal enclosing ball in kernel feature space.
%
% Dual of SVDD model
% min 0.5*x'(2*K)x - x'*diag(K)
% s.t. 0