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/354741/10329418
m demos.m
function tbxStruct=Demos
% DEMOS Demo List information for OSV SVM Classifier Matlab Toolbox
if nargout==0, demo toolbox; return; end
tbxStruct.Name='OSU SVM Classifier';
tbxStruct.Type='
www.eeworm.com/read/421949/10675945
m demos.m
function tbxStruct=Demos
% DEMOS Demo List information for OSV SVM Classifier Matlab Toolbox
if nargout==0, demo toolbox; return; end
tbxStruct.Name='OSU SVM Classifier';
tbxStruct.Type='
www.eeworm.com/read/349725/10801981
m demos.m
function tbxStruct=Demos
% DEMOS Demo List information for OSV SVM Classifier Matlab Toolbox
if nargout==0, demo toolbox; return; end
tbxStruct.Name='OSU SVM Classifier';
tbxStruct.Type='
www.eeworm.com/read/299984/7140693
m svc_nu.m
%SVC_NU Support Vector Classifier: NU algorithm
%
% This routine is outdated, use NUSVC instead
%
% [W,J,C] = SVC(A,TYPE,PAR,NU,MC,PD)
%
% INPUT
% A Dataset
% TYPE Type of the kernel (opti
www.eeworm.com/read/460435/7251169
m svc_nu.m
%SVC_NU Support Vector Classifier: NU algorithm
%
% This routine is outdated, use NUSVC instead
%
% [W,J,C] = SVC(A,TYPE,PAR,NU,MC,PD)
%
% INPUT
% A Dataset
% TYPE Type of the kernel (opti
www.eeworm.com/read/450608/7480099
m loglc.m
%LOGLC Logistic Linear Classifier
%
% W = LOGLC(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% W Logistic linear classifier
%
% DESCRIPTION
% Computation of the linear classifier for the dataset
www.eeworm.com/read/450608/7480120
m svc.m
%SVC Support Vector Classifier
%
% [W,J] = SVC(A,TYPE,PAR,C)
%
% INPUT
% A Dataset
% TYPE Type of the kernel (optional; default: 'p')
% PAR Kernel parameter (optional; default: 1)
% C
www.eeworm.com/read/450608/7480127
m nmc.m
%NMC Nearest Mean Classifier
%
% W = NMC(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% W Nearest Mean Classifier
%
% DESCRIPTION
% Computation of the nearest mean classifier between the classe
www.eeworm.com/read/450608/7480402
m mogc.m
%MOGC Mixture of Gaussian classifier
%
% W = MOGC(A,N)
% W = A*MOGC([],N);
%
% INPUT
% A Dataset
% N Number of mixtures (optional; default 2)
% OUTPUT
%
% DESCRIPTION
% For each class j
www.eeworm.com/read/450608/7480564
m svc_nu.m
%SVC_NU Support Vector Classifier: NU algorithm
%
% [W,J,C] = SVC(A,TYPE,PAR,NU,MC,PD)
%
% INPUT
% A Dataset
% TYPE Type of the kernel (optional; default: 'p')
% PAR Kernel parameter (o