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/428451/8867194
m latentlssvm.m
function [zt,model] = latentlssvm(varargin)
% Calculate the latent variables of the LS-SVM classifier at the given test data
%
% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)
www.eeworm.com/read/427586/8931934
m latentlssvm.m
function [zt,model] = latentlssvm(varargin)
% Calculate the latent variables of the LS-SVM classifier at the given test data
%
% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)
www.eeworm.com/read/183445/9158635
m latentlssvm.m
function [zt,model] = latentlssvm(varargin)
% Calculate the latent variables of the LS-SVM classifier at the given test data
%
% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)
www.eeworm.com/read/374698/9388831
m latentlssvm.m
function [zt,model] = latentlssvm(varargin)
% Calculate the latent variables of the LS-SVM classifier at the given test data
%
% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)
www.eeworm.com/read/360995/10069952
m plotroc.m
function h = plotroc(e,varargin)
%PLOTROC Draw an ROC curve
%
% H = PLOTROC(W,A)
% H = PLOTROC(E)
%
% Plot the roc curve of E according to the 'traditional' way: on the x
% axis we put the fal
www.eeworm.com/read/360895/10072656
m latentlssvm.m
function [zt,model] = latentlssvm(varargin)
% Calculate the latent variables of the LS-SVM classifier at the given test data
%
% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)
www.eeworm.com/read/278889/10490469
m latentlssvm.m
function [zt,model] = latentlssvm(varargin)
% Calculate the latent variables of the LS-SVM classifier at the given test data
%
% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)
www.eeworm.com/read/421949/10676035
m latentlssvm.m
function [zt,model] = latentlssvm(varargin)
% Calculate the latent variables of the LS-SVM classifier at the given test data
%
% >> Zt = latentlssvm({X,Y,'classifier',gam,sig2,kernel}, {alpha,b}, Xt)
www.eeworm.com/read/418695/10935417
m binm.m
%BINM Binary mapping for classifier outcomes
%
% W = W*binm
%
% Binary transformation of a map or a classifier.
%
% binm transforms the outcomes of the classifier or map
% to binary using the maxim
www.eeworm.com/read/299984/7139964
m minc.m
%MINC Minimum combining classifier
%
% W = MINC(V)
% W = V*MINC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Minimum combining classifier on V
%
% DESCRIPTION
% If V = [V1,V2,V3, ...